[Git][debian-gis-team/trollimage][master] 5 commits: New upstream version 1.22.0
Antonio Valentino (@antonio.valentino)
gitlab at salsa.debian.org
Fri Nov 24 07:37:38 GMT 2023
Antonio Valentino pushed to branch master at Debian GIS Project / trollimage
Commits:
46d69694 by Antonio Valentino at 2023-11-24T07:22:57+00:00
New upstream version 1.22.0
- - - - -
2257b5fb by Antonio Valentino at 2023-11-24T07:23:04+00:00
Update upstream source from tag 'upstream/1.22.0'
Update to upstream version '1.22.0'
with Debian dir 53c7d633911f298bafa8fec5eb95ac39d135ce22
- - - - -
be1129c2 by Antonio Valentino at 2023-11-24T07:27:01+00:00
New upstream release
- - - - -
9791bdf3 by Antonio Valentino at 2023-11-24T07:28:15+00:00
Refresh all patches
- - - - -
233e21d4 by Antonio Valentino at 2023-11-24T07:36:01+00:00
Skip tests with too strict numerical accuracy
- - - - -
13 changed files:
- .github/workflows/ci.yaml
- .github/workflows/deploy.yaml
- .readthedocs.yml
- CHANGELOG.md
- debian/changelog
- debian/patches/0001-No-display.patch
- debian/rules
- + load_colorbrewer_colormaps.py
- pyproject.toml
- trollimage/colormap.py
- trollimage/tests/test_image.py
- trollimage/version.py
- trollimage/xrimage.py
Changes:
=====================================
.github/workflows/ci.yaml
=====================================
@@ -10,7 +10,7 @@ jobs:
fail-fast: true
matrix:
os: ["windows-latest", "ubuntu-latest", "macos-latest"]
- python-version: ["3.9", "3.10", "3.11"]
+ python-version: ["3.9", "3.11", "3.12"]
experimental: [false]
include:
- python-version: "3.11"
=====================================
.github/workflows/deploy.yaml
=====================================
@@ -28,19 +28,38 @@ jobs:
path: dist/*.tar.gz
build_wheels:
- name: Build wheels on ${{ matrix.os }}
+ name: "Build wheels on ${{ matrix.os }} ${{ matrix.cibw_archs }}"
runs-on: ${{ matrix.os }}
strategy:
+ fail-fast: false
matrix:
- os: [ubuntu-20.04, windows-2019, macOS-11]
+ include:
+ - os: windows-2019
+ cibw_archs: "AMD64 ARM64"
+ - os: macos-11
+ cibw_archs: "x86_64 arm64"
+ - os: "ubuntu-20.04"
+ cibw_archs: "aarch64"
+ - os: "ubuntu-20.04"
+ cibw_archs: "x86_64"
steps:
- uses: actions/checkout at v4
+ - run: |
+ git fetch --prune --unshallow
+
+ - name: Set up QEMU
+ if: runner.os == 'Linux'
+ uses: docker/setup-qemu-action at v3
+ with:
+ platforms: all
- name: Build wheels
- uses: pypa/cibuildwheel at v2.15.0
+ uses: pypa/cibuildwheel at v2.16.2
env:
- CIBW_SKIP: "cp36-* cp37-* cp38-* cp312-* pp* *-i686"
+ CIBW_SKIP: "cp36-* cp37-* cp38-* pp* *-manylinux_i686 *-musllinux_i686 *-musllinux_aarch64 *-win32"
+ CIBW_ARCHS: "${{ matrix.cibw_archs }}"
+ CIBW_TEST_SKIP: "*_arm64 *_universal2:arm64"
- uses: actions/upload-artifact at v3
with:
=====================================
.readthedocs.yml
=====================================
@@ -3,7 +3,12 @@ version: 2
build:
os: "ubuntu-20.04"
tools:
- python: "3.10"
+ python: "3.11"
+ jobs:
+ post_checkout:
+ - git fetch --tags
+ pre_install:
+ - git update-index --assume-unchanged rtd_requirements.txt doc/conf.py
sphinx:
configuration: doc/conf.py
fail_on_warning: true
=====================================
CHANGELOG.md
=====================================
@@ -1,3 +1,21 @@
+## Version 1.22.0 (2023/11/23)
+
+
+### Pull Requests Merged
+
+#### Bugs fixed
+
+* [PR 145](https://github.com/pytroll/trollimage/pull/145) - Do not apply linear stretch to alpha band
+
+#### Features added
+
+* [PR 151](https://github.com/pytroll/trollimage/pull/151) - Preserve dtypes in XRImage "enhancements"
+* [PR 150](https://github.com/pytroll/trollimage/pull/150) - Keep the original dtype of the data when stretching
+* [PR 141](https://github.com/pytroll/trollimage/pull/141) - Update colorbrew colormaps to be more accurate
+
+In this release 4 pull requests were closed.
+
+
## Version 1.21.0 (2023/09/04)
### Issues Closed
=====================================
debian/changelog
=====================================
@@ -1,3 +1,14 @@
+trollimage (1.22.0-1) UNRELEASED; urgency=medium
+
+ * New upstream release.
+ * debian/patches:
+ - Refresh all patches.
+ * debian/rules:
+ - Skip testt with too strict numerical acciracy
+ (test_gamma_per_channel).
+
+ -- Antonio Valentino <antonio.valentino at tiscali.it> Fri, 24 Nov 2023 07:23:16 +0000
+
trollimage (1.21.0-1) unstable; urgency=medium
[ Bas Couwenberg ]
=====================================
debian/patches/0001-No-display.patch
=====================================
@@ -8,10 +8,10 @@ Skip tests that require display.
1 file changed, 1 insertion(+)
diff --git a/trollimage/tests/test_image.py b/trollimage/tests/test_image.py
-index be83bfa..c9b8754 100644
+index 3421efe..64650c1 100644
--- a/trollimage/tests/test_image.py
+++ b/trollimage/tests/test_image.py
-@@ -1936,6 +1936,7 @@ class TestXRImage:
+@@ -2034,6 +2034,7 @@ class TestXRImage:
"""Test putalpha."""
pass
=====================================
debian/rules
=====================================
@@ -6,7 +6,11 @@
export DEB_BUILD_MAINT_OPTIONS=hardening=+all
export PYBUILD_NAME=trollimage
-export PYBUILD_TEST_ARGS=--pyargs ${PYBUILD_NAME}
+
+# skip tests with too strict numerical accuracy
+export PYBUILD_TEST_ARGS=\
+-k "not test_gamma_per_channel" \
+--pyargs ${PYBUILD_NAME}
%:
dh $@ --buildsystem=pybuild
=====================================
load_colorbrewer_colormaps.py
=====================================
@@ -0,0 +1,76 @@
+"""Helper script to convert colormaps from https://colorbrewer2.org into trollimage Colormap code.
+
+The text output by this script should be copied to the ``trollimage/colormap.py``.
+
+"""
+
+import json
+import sys
+import urllib.request
+
+JSON_URL = "https://raw.githubusercontent.com/axismaps/colorbrewer/master/export/colorbrewer.json"
+
+
+def main():
+ """Print python code version of trollimage Colormap objects for each colorbrewer colormap."""
+ cmap_groups = _load_colormap_info_from_colorbrewer()
+ _print_colormap_group(cmap_groups["seq"], "Sequential", "sequential_colormaps")
+ _print_colormap_group(cmap_groups["div"], "Diverging", "diverging_colormaps")
+ _print_colormap_group(cmap_groups["qual"], "Qualitative", "qualitative_colormaps",
+ normalize_values=False)
+
+
+def _load_colormap_info_from_colorbrewer() -> dict[str, dict[str, list]]:
+ with urllib.request.urlopen(JSON_URL) as json_file: # nosec: B310
+ colorbrewer_dict = json.load(json_file)
+ cmap_groups: dict[str, dict[str, list]] = {"div": {}, "seq": {}, "qual": {}}
+ for cmap_name, cmap_info in colorbrewer_dict.items():
+ max_colors = max((num_colors_str for num_colors_str in cmap_info.keys() if num_colors_str != "type"),
+ key=lambda num_color_str: int(num_color_str))
+ cmap_colors = cmap_info[max_colors]
+ color_tuples = [rgb_color_str.replace("rgb(", "").replace(")", "").split(",")
+ for rgb_color_str in cmap_colors]
+ cmap_groups[cmap_info["type"]][cmap_name.lower()] = color_tuples
+ return cmap_groups
+
+
+def _print_colormap_group(cmap_group: dict[str, list], human_group_name: str, group_var_name: str,
+ normalize_values: bool = True) -> None:
+ print(f"# * {human_group_name} Colormaps *\n")
+ for cmap_name, cmap_colors in sorted(cmap_group.items()):
+ cmap_values, color_human_strings = _color_info_as_human_friendly_strings(cmap_colors, normalize_values)
+ _print_single_colormap(cmap_name, cmap_values, color_human_strings)
+
+ print(f"{group_var_name} = {{")
+ for cmap_name in sorted(cmap_group.keys()):
+ print(f" \"{cmap_name}\": {cmap_name},")
+ print("}\n")
+
+
+def _color_info_as_human_friendly_strings(
+ cmap_colors: list[tuple[int, int, int]],
+ normalize_values: bool,
+) -> tuple[list[str], list[tuple[str, str, str]]]:
+ num_colors = len(cmap_colors)
+ color_human_strings = [
+ (f"{rgb_color[0]} / 255", f"{rgb_color[1]} / 255", f"{rgb_color[2]} / 255")
+ for rgb_color in cmap_colors
+ ]
+ cmap_values = [str(color_idx) for color_idx in range(num_colors)]
+ if normalize_values:
+ # 0 - 1 normalized values for non-qualitative colormaps
+ cmap_values = [f"{cval} / {num_colors - 1}" for cval in cmap_values]
+ return cmap_values, color_human_strings
+
+
+def _print_single_colormap(cmap_name: str, cmap_values: list[str], cmap_colors: list[tuple[str, str, str]]) -> None:
+ cmap_pairs = [(cval, rgb_color) for cval, rgb_color in zip(cmap_values, cmap_colors)]
+ print(f"{cmap_name} = Colormap(")
+ for cmap_value_str, cmap_color_tuple in cmap_pairs:
+ print(f" ({cmap_value_str}, "
+ f"({cmap_color_tuple[0]}, {cmap_color_tuple[1]}, {cmap_color_tuple[2]})),")
+ print(")\n")
+
+
+if __name__ == "__main__":
+ sys.exit(main())
=====================================
pyproject.toml
=====================================
@@ -1,5 +1,12 @@
[build-system]
-requires = ["setuptools", "wheel", "oldest-supported-numpy", "Cython>=3", "versioneer"]
+requires = [
+ "wheel",
+ 'numpy; python_full_version<"3.12.0rc1"',
+ 'numpy>=1.26.0rc1; python_full_version>="3.12.0rc1"',
+ "setuptools>=42",
+ "versioneer",
+ "Cython>=3.0.0"
+]
build-backend = "setuptools.build_meta"
[tool.coverage.run]
=====================================
trollimage/colormap.py
=====================================
@@ -782,297 +782,494 @@ rainbow = Colormap((0.000, (0.0, 0.0, 0.5)),
# * Colors from www.ColorBrewer.org by Cynthia A. Brewer, Geography,
# * Pennsylvania State University.
-# * Single hue *
-
-blues = Colormap((0.000, (247 / 255.0, 251 / 255.0, 1.0)),
- (1.000, (8 / 255.0, 48 / 255.0, 107 / 255.0)))
-
-greens = Colormap((0.000, (247 / 255.0, 252 / 255.0, 245 / 255.0)),
- (1.000, (0.0, 68 / 255.0, 27 / 255.0)))
-
-greys = Colormap((0.0, (1.0, 1.0, 1.0)),
- (1.0, (0.0, 0.0, 0.0)))
-
-oranges = Colormap((0.0, (1.0, 245 / 255.0, 235 / 255.0)),
- (1.0, (127 / 255.0, 39 / 255.0, 4 / 255.0)))
-
-purples = Colormap((0.0, (252 / 255.0, 251 / 255.0, 253 / 255.0)),
- (1.0, (63 / 255.0, 0.0, 125 / 255.0)))
-
-reds = Colormap((0.0, (1.0, 245 / 255.0, 240 / 255.0)),
- (1.0, (103 / 255.0, 0.0, 13 / 255.0)))
-
-# * Multihue *
-
-# BuGn
-
-bugn = Colormap((0.000, (247 / 255.0, 252 / 255.0, 253 / 255.0)),
- (1.000, (0.0, 68 / 255.0, 27 / 255.0)))
-
-# BuPu
-
-bupu = Colormap((0.000, (247 / 255.0, 252 / 255.0, 253 / 255.0)),
- (1.000, (77 / 255.0, 0.0, 75 / 255.0)))
-
-# GnBu
-
-gnbu = Colormap((0.000, (247 / 255.0, 252 / 255.0, 240 / 255.0)),
- (1.000, (8 / 255.0, 64 / 255.0, 129 / 255.0)))
-
-# OrRd
-
-orrd = Colormap((0.000, (255 / 255.0, 247 / 255.0, 236 / 255.0)),
- (1.000, (127 / 255.0, 0.0, 0.0)))
-
-# PuBu
-
-pubu = Colormap((0.000, (1.0, 247 / 255.0, 251 / 255.0)),
- (0.500, (116 / 255.0, 169 / 255.0, 207 / 255.0)),
- (1.000, (2 / 255.0, 56 / 255.0, 88 / 255.0)))
-
-# PuBuGn
-
-pubugn = Colormap((0.000, (1.0, 247 / 255.0, 251 / 255.0)),
- (0.500, (103 / 255.0, 169 / 255.0, 207 / 255.0)),
- (1.000, (1 / 255.0, 70 / 255.0, 54 / 255.0)))
-
-# PuRd
-
-purd = Colormap((0.000, (247 / 255.0, 244 / 255.0, 249 / 255.0)),
- (1.000, (103 / 255.0, 0.0, 31 / 255.0)))
-
-# RdPu
-
-rdpu = Colormap((0.000, (1.0, 247 / 255.0, 243 / 255.0)),
- (1.000, (73 / 255.0, 0.0, 106 / 255.0)))
-
-# YlGn
-
-ylgn = Colormap((0.000, (1.0, 1.0, 229 / 255.0)),
- (1.000, (0.0, 69 / 255.0, 41 / 255.0)))
-
-# YlGnBu
-
-ylgnbu = Colormap((0.000, (1.0, 1.0, 217 / 255.0)),
- (0.500, (65 / 255.0, 182 / 255.0, 196 / 255.0)),
- (1.000, (8 / 255.0, 29 / 255.0, 88 / 255.0)))
-
-# YlOrBr
-
-ylorbr = Colormap((0.000, (1.0, 1.0, 229 / 255.0)),
- (0.500, (254 / 255.0, 153 / 255.0, 41 / 255.0)),
- (1.000, (102 / 255.0, 37 / 255.0, 6 / 255.0)))
-
-# YlOrRd
-
-ylorrd = Colormap((0.000, (1.0, 1.0, 204 / 255.0)),
- (0.500, (254 / 255.0, 141 / 255.0, 60 / 255.0)),
- (1.000, (128 / 255.0, 0.0, 38 / 255.0)))
-
-sequential_colormaps = [blues, greens, greys, oranges, purples, reds,
- bugn, bupu, gnbu, orrd, pubu, pubugn, purd, rdpu,
- ylgn, ylgnbu, ylorbr, ylorrd]
-
-# * Diverging *
-
-brbg = Colormap((0.0, (84 / 255.0, 48 / 255.0, 5 / 255.0)),
- (0.1, (140 / 255.0, 81 / 255.0, 10 / 255.0)),
- (0.2, (191 / 255.0, 129 / 255.0, 45 / 255.0)),
- (0.3, (223 / 255.0, 194 / 255.0, 125 / 255.0)),
- (0.4, (246 / 255.0, 232 / 255.0, 195 / 255.0)),
- (0.5, (245 / 255.0, 245 / 255.0, 245 / 255.0)),
- (0.6, (199 / 255.0, 234 / 255.0, 229 / 255.0)),
- (0.7, (128 / 255.0, 205 / 255.0, 193 / 255.0)),
- (0.8, (53 / 255.0, 151 / 255.0, 143 / 255.0)),
- (0.9, (1 / 255.0, 102 / 255.0, 94 / 255.0)),
- (1.0, (0 / 255.0, 60 / 255.0, 48 / 255.0)))
-
-piyg = Colormap((0.0, (142 / 255.0, 1 / 255.0, 82 / 255.0)),
- (0.1, (197 / 255.0, 27 / 255.0, 125 / 255.0)),
- (0.2, (222 / 255.0, 119 / 255.0, 174 / 255.0)),
- (0.3, (241 / 255.0, 182 / 255.0, 218 / 255.0)),
- (0.4, (253 / 255.0, 224 / 255.0, 239 / 255.0)),
- (0.5, (247 / 255.0, 247 / 255.0, 247 / 255.0)),
- (0.6, (230 / 255.0, 245 / 255.0, 208 / 255.0)),
- (0.7, (184 / 255.0, 225 / 255.0, 134 / 255.0)),
- (0.8, (127 / 255.0, 188 / 255.0, 65 / 255.0)),
- (0.9, (77 / 255.0, 146 / 255.0, 33 / 255.0)),
- (1.0, (39 / 255.0, 100 / 255.0, 25 / 255.0)))
-
-prgn = Colormap((0.0, (64 / 255.0, 0 / 255.0, 75 / 255.0)),
- (0.1, (118 / 255.0, 42 / 255.0, 131 / 255.0)),
- (0.2, (153 / 255.0, 112 / 255.0, 171 / 255.0)),
- (0.3, (194 / 255.0, 165 / 255.0, 207 / 255.0)),
- (0.4, (231 / 255.0, 212 / 255.0, 232 / 255.0)),
- (0.5, (247 / 255.0, 247 / 255.0, 247 / 255.0)),
- (0.6, (217 / 255.0, 240 / 255.0, 211 / 255.0)),
- (0.7, (166 / 255.0, 219 / 255.0, 160 / 255.0)),
- (0.8, (90 / 255.0, 174 / 255.0, 97 / 255.0)),
- (0.9, (27 / 255.0, 120 / 255.0, 55 / 255.0)),
- (1.0, (0 / 255.0, 68 / 255.0, 27 / 255.0)))
-
-puor = Colormap((0.0, (127 / 255.0, 59 / 255.0, 8 / 255.0)),
- (0.1, (179 / 255.0, 88 / 255.0, 6 / 255.0)),
- (0.2, (224 / 255.0, 130 / 255.0, 20 / 255.0)),
- (0.3, (253 / 255.0, 184 / 255.0, 99 / 255.0)),
- (0.4, (254 / 255.0, 224 / 255.0, 182 / 255.0)),
- (0.5, (247 / 255.0, 247 / 255.0, 247 / 255.0)),
- (0.6, (216 / 255.0, 218 / 255.0, 235 / 255.0)),
- (0.7, (178 / 255.0, 171 / 255.0, 210 / 255.0)),
- (0.8, (128 / 255.0, 115 / 255.0, 172 / 255.0)),
- (0.9, (84 / 255.0, 39 / 255.0, 136 / 255.0)),
- (1.0, (45 / 255.0, 0 / 255.0, 75 / 255.0)))
-
-rdbu = Colormap((0.0, (103 / 255.0, 0 / 255.0, 31 / 255.0)),
- (0.1, (178 / 255.0, 24 / 255.0, 43 / 255.0)),
- (0.2, (214 / 255.0, 96 / 255.0, 77 / 255.0)),
- (0.3, (244 / 255.0, 165 / 255.0, 130 / 255.0)),
- (0.4, (253 / 255.0, 219 / 255.0, 199 / 255.0)),
- (0.5, (247 / 255.0, 247 / 255.0, 247 / 255.0)),
- (0.6, (209 / 255.0, 229 / 255.0, 240 / 255.0)),
- (0.7, (146 / 255.0, 197 / 255.0, 222 / 255.0)),
- (0.8, (67 / 255.0, 147 / 255.0, 195 / 255.0)),
- (0.9, (33 / 255.0, 102 / 255.0, 172 / 255.0)),
- (1.0, (5 / 255.0, 48 / 255.0, 97 / 255.0)))
-
-rdgy = Colormap((0.0, (103 / 255.0, 0 / 255.0, 31 / 255.0)),
- (0.1, (178 / 255.0, 24 / 255.0, 43 / 255.0)),
- (0.2, (214 / 255.0, 96 / 255.0, 77 / 255.0)),
- (0.3, (244 / 255.0, 165 / 255.0, 130 / 255.0)),
- (0.4, (253 / 255.0, 219 / 255.0, 199 / 255.0)),
- (0.5, (255 / 255.0, 255 / 255.0, 255 / 255.0)),
- (0.6, (224 / 255.0, 224 / 255.0, 224 / 255.0)),
- (0.7, (186 / 255.0, 186 / 255.0, 186 / 255.0)),
- (0.8, (135 / 255.0, 135 / 255.0, 135 / 255.0)),
- (0.9, (77 / 255.0, 77 / 255.0, 77 / 255.0)),
- (1.0, (26 / 255.0, 26 / 255.0, 26 / 255.0)))
-
-rdylbu = Colormap((0.0, (165 / 255.0, 0 / 255.0, 38 / 255.0)),
- (0.1, (215 / 255.0, 48 / 255.0, 39 / 255.0)),
- (0.2, (244 / 255.0, 109 / 255.0, 67 / 255.0)),
- (0.3, (253 / 255.0, 174 / 255.0, 97 / 255.0)),
- (0.4, (254 / 255.0, 224 / 255.0, 144 / 255.0)),
- (0.5, (255 / 255.0, 255 / 255.0, 191 / 255.0)),
- (0.6, (224 / 255.0, 243 / 255.0, 248 / 255.0)),
- (0.7, (171 / 255.0, 217 / 255.0, 233 / 255.0)),
- (0.8, (116 / 255.0, 173 / 255.0, 209 / 255.0)),
- (0.9, (69 / 255.0, 117 / 255.0, 180 / 255.0)),
- (1.0, (49 / 255.0, 54 / 255.0, 149 / 255.0)))
-
-rdylgn = Colormap((0.0, (165 / 255.0, 0 / 255.0, 38 / 255.0)),
- (0.1, (215 / 255.0, 48 / 255.0, 39 / 255.0)),
- (0.2, (244 / 255.0, 109 / 255.0, 67 / 255.0)),
- (0.3, (253 / 255.0, 174 / 255.0, 97 / 255.0)),
- (0.4, (254 / 255.0, 224 / 255.0, 139 / 255.0)),
- (0.5, (255 / 255.0, 255 / 255.0, 191 / 255.0)),
- (0.6, (217 / 255.0, 239 / 255.0, 139 / 255.0)),
- (0.7, (166 / 255.0, 217 / 255.0, 106 / 255.0)),
- (0.8, (102 / 255.0, 189 / 255.0, 99 / 255.0)),
- (0.9, (26 / 255.0, 152 / 255.0, 80 / 255.0)),
- (1.0, (0 / 255.0, 104 / 255.0, 55 / 255.0)))
-
-spectral = Colormap((0.0, (158 / 255.0, 1 / 255.0, 66 / 255.0)),
- (0.1, (213 / 255.0, 62 / 255.0, 79 / 255.0)),
- (0.2, (244 / 255.0, 109 / 255.0, 67 / 255.0)),
- (0.3, (253 / 255.0, 174 / 255.0, 97 / 255.0)),
- (0.4, (254 / 255.0, 224 / 255.0, 139 / 255.0)),
- (0.5, (255 / 255.0, 255 / 255.0, 191 / 255.0)),
- (0.6, (230 / 255.0, 245 / 255.0, 152 / 255.0)),
- (0.7, (171 / 255.0, 221 / 255.0, 164 / 255.0)),
- (0.8, (102 / 255.0, 194 / 255.0, 165 / 255.0)),
- (0.9, (50 / 255.0, 136 / 255.0, 189 / 255.0)),
- (1.0, (94 / 255.0, 79 / 255.0, 162 / 255.0)))
-
-diverging_colormaps = [brbg, piyg, prgn, puor, rdbu, rdgy, rdylbu, rdylgn,
- spectral]
-
-# * qualitative colormaps *
-
-set1 = Colormap((0, (228 / 255.0, 26 / 255.0, 28 / 255.0)),
- (1, (55 / 255.0, 126 / 255.0, 184 / 255.0)),
- (2, (77 / 255.0, 175 / 255.0, 74 / 255.0)),
- (3, (152 / 255.0, 78 / 255.0, 163 / 255.0)),
- (4, (255 / 255.0, 127 / 255.0, 0 / 255.0)),
- (5, (255 / 255.0, 255 / 255.0, 51 / 255.0)),
- (6, (166 / 255.0, 86 / 255.0, 40 / 255.0)),
- (7, (247 / 255.0, 129 / 255.0, 191 / 255.0)),
- (8, (153 / 255.0, 153 / 255.0, 153 / 255.0)))
-
-set2 = Colormap((0, (102 / 255.0, 194 / 255.0, 165 / 255.0)),
- (1, (252 / 255.0, 141 / 255.0, 98 / 255.0)),
- (2, (141 / 255.0, 160 / 255.0, 203 / 255.0)),
- (3, (231 / 255.0, 138 / 255.0, 195 / 255.0)),
- (4, (166 / 255.0, 216 / 255.0, 84 / 255.0)),
- (5, (255 / 255.0, 217 / 255.0, 47 / 255.0)),
- (6, (229 / 255.0, 196 / 255.0, 148 / 255.0)),
- (7, (179 / 255.0, 179 / 255.0, 179 / 255.0)))
-
-set3 = Colormap((0, (141 / 255.0, 211 / 255.0, 199 / 255.0)),
- (1, (255 / 255.0, 255 / 255.0, 179 / 255.0)),
- (2, (190 / 255.0, 186 / 255.0, 218 / 255.0)),
- (3, (251 / 255.0, 128 / 255.0, 114 / 255.0)),
- (4, (128 / 255.0, 177 / 255.0, 211 / 255.0)),
- (5, (253 / 255.0, 180 / 255.0, 98 / 255.0)),
- (6, (179 / 255.0, 222 / 255.0, 105 / 255.0)),
- (7, (252 / 255.0, 205 / 255.0, 229 / 255.0)),
- (8, (217 / 255.0, 217 / 255.0, 217 / 255.0)),
- (9, (188 / 255.0, 128 / 255.0, 189 / 255.0)),
- (10, (204 / 255.0, 235 / 255.0, 197 / 255.0)),
- (11, (255 / 255.0, 237 / 255.0, 111 / 255.0)))
-
-paired = Colormap((0, (166 / 255.0, 206 / 255.0, 227 / 255.0)),
- (1, (31 / 255.0, 120 / 255.0, 180 / 255.0)),
- (2, (178 / 255.0, 223 / 255.0, 138 / 255.0)),
- (3, (51 / 255.0, 160 / 255.0, 44 / 255.0)),
- (4, (251 / 255.0, 154 / 255.0, 153 / 255.0)),
- (5, (227 / 255.0, 26 / 255.0, 28 / 255.0)),
- (6, (253 / 255.0, 191 / 255.0, 111 / 255.0)),
- (7, (255 / 255.0, 127 / 255.0, 0 / 255.0)),
- (8, (202 / 255.0, 178 / 255.0, 214 / 255.0)),
- (9, (106 / 255.0, 61 / 255.0, 154 / 255.0)),
- (10, (255 / 255.0, 255 / 255.0, 153 / 255.0)),
- (11, (177 / 255.0, 89 / 255.0, 40 / 255.0)))
-
-accent = Colormap((0, (127 / 255.0, 201 / 255.0, 127 / 255.0)),
- (1, (190 / 255.0, 174 / 255.0, 212 / 255.0)),
- (2, (253 / 255.0, 192 / 255.0, 134 / 255.0)),
- (3, (255 / 255.0, 255 / 255.0, 153 / 255.0)),
- (4, (56 / 255.0, 108 / 255.0, 176 / 255.0)),
- (5, (240 / 255.0, 2 / 255.0, 127 / 255.0)),
- (6, (191 / 255.0, 91 / 255.0, 23 / 255.0)),
- (7, (102 / 255.0, 102 / 255.0, 102 / 255.0)))
-
-dark2 = Colormap((0, (27 / 255.0, 158 / 255.0, 119 / 255.0)),
- (1, (217 / 255.0, 95 / 255.0, 2 / 255.0)),
- (2, (117 / 255.0, 112 / 255.0, 179 / 255.0)),
- (3, (231 / 255.0, 41 / 255.0, 138 / 255.0)),
- (4, (102 / 255.0, 166 / 255.0, 30 / 255.0)),
- (5, (230 / 255.0, 171 / 255.0, 2 / 255.0)),
- (6, (166 / 255.0, 118 / 255.0, 29 / 255.0)),
- (7, (102 / 255.0, 102 / 255.0, 102 / 255.0)))
-
-pastel1 = Colormap((0, (251 / 255.0, 180 / 255.0, 174 / 255.0)),
- (1, (179 / 255.0, 205 / 255.0, 227 / 255.0)),
- (2, (204 / 255.0, 235 / 255.0, 197 / 255.0)),
- (3, (222 / 255.0, 203 / 255.0, 228 / 255.0)),
- (4, (254 / 255.0, 217 / 255.0, 166 / 255.0)),
- (5, (255 / 255.0, 255 / 255.0, 204 / 255.0)),
- (6, (229 / 255.0, 216 / 255.0, 189 / 255.0)),
- (7, (253 / 255.0, 218 / 255.0, 236 / 255.0)),
- (8, (242 / 255.0, 242 / 255.0, 242 / 255.0)))
-
-pastel2 = Colormap((0, (179 / 255.0, 226 / 255.0, 205 / 255.0)),
- (1, (253 / 255.0, 205 / 255.0, 172 / 255.0)),
- (2, (203 / 255.0, 213 / 255.0, 232 / 255.0)),
- (3, (244 / 255.0, 202 / 255.0, 228 / 255.0)),
- (4, (230 / 255.0, 245 / 255.0, 201 / 255.0)),
- (5, (255 / 255.0, 242 / 255.0, 174 / 255.0)),
- (6, (241 / 255.0, 226 / 255.0, 204 / 255.0)),
- (7, (204 / 255.0, 204 / 255.0, 204 / 255.0)))
-
-qualitative_colormaps = [set1, set2, set3,
- paired, accent, dark2,
- pastel1, pastel2]
+# * Sequential Colormaps *
+
+blues = Colormap(
+ (0 / 8, (247 / 255, 251 / 255, 255 / 255)),
+ (1 / 8, (222 / 255, 235 / 255, 247 / 255)),
+ (2 / 8, (198 / 255, 219 / 255, 239 / 255)),
+ (3 / 8, (158 / 255, 202 / 255, 225 / 255)),
+ (4 / 8, (107 / 255, 174 / 255, 214 / 255)),
+ (5 / 8, (66 / 255, 146 / 255, 198 / 255)),
+ (6 / 8, (33 / 255, 113 / 255, 181 / 255)),
+ (7 / 8, (8 / 255, 81 / 255, 156 / 255)),
+ (8 / 8, (8 / 255, 48 / 255, 107 / 255)),
+)
+
+bugn = Colormap(
+ (0 / 8, (247 / 255, 252 / 255, 253 / 255)),
+ (1 / 8, (229 / 255, 245 / 255, 249 / 255)),
+ (2 / 8, (204 / 255, 236 / 255, 230 / 255)),
+ (3 / 8, (153 / 255, 216 / 255, 201 / 255)),
+ (4 / 8, (102 / 255, 194 / 255, 164 / 255)),
+ (5 / 8, (65 / 255, 174 / 255, 118 / 255)),
+ (6 / 8, (35 / 255, 139 / 255, 69 / 255)),
+ (7 / 8, (0 / 255, 109 / 255, 44 / 255)),
+ (8 / 8, (0 / 255, 68 / 255, 27 / 255)),
+)
+
+bupu = Colormap(
+ (0 / 8, (247 / 255, 252 / 255, 253 / 255)),
+ (1 / 8, (224 / 255, 236 / 255, 244 / 255)),
+ (2 / 8, (191 / 255, 211 / 255, 230 / 255)),
+ (3 / 8, (158 / 255, 188 / 255, 218 / 255)),
+ (4 / 8, (140 / 255, 150 / 255, 198 / 255)),
+ (5 / 8, (140 / 255, 107 / 255, 177 / 255)),
+ (6 / 8, (136 / 255, 65 / 255, 157 / 255)),
+ (7 / 8, (129 / 255, 15 / 255, 124 / 255)),
+ (8 / 8, (77 / 255, 0 / 255, 75 / 255)),
+)
+
+gnbu = Colormap(
+ (0 / 8, (247 / 255, 252 / 255, 240 / 255)),
+ (1 / 8, (224 / 255, 243 / 255, 219 / 255)),
+ (2 / 8, (204 / 255, 235 / 255, 197 / 255)),
+ (3 / 8, (168 / 255, 221 / 255, 181 / 255)),
+ (4 / 8, (123 / 255, 204 / 255, 196 / 255)),
+ (5 / 8, (78 / 255, 179 / 255, 211 / 255)),
+ (6 / 8, (43 / 255, 140 / 255, 190 / 255)),
+ (7 / 8, (8 / 255, 104 / 255, 172 / 255)),
+ (8 / 8, (8 / 255, 64 / 255, 129 / 255)),
+)
+
+greens = Colormap(
+ (0 / 8, (247 / 255, 252 / 255, 245 / 255)),
+ (1 / 8, (229 / 255, 245 / 255, 224 / 255)),
+ (2 / 8, (199 / 255, 233 / 255, 192 / 255)),
+ (3 / 8, (161 / 255, 217 / 255, 155 / 255)),
+ (4 / 8, (116 / 255, 196 / 255, 118 / 255)),
+ (5 / 8, (65 / 255, 171 / 255, 93 / 255)),
+ (6 / 8, (35 / 255, 139 / 255, 69 / 255)),
+ (7 / 8, (0 / 255, 109 / 255, 44 / 255)),
+ (8 / 8, (0 / 255, 68 / 255, 27 / 255)),
+)
+
+greys = Colormap(
+ (0 / 8, (255 / 255, 255 / 255, 255 / 255)),
+ (1 / 8, (240 / 255, 240 / 255, 240 / 255)),
+ (2 / 8, (217 / 255, 217 / 255, 217 / 255)),
+ (3 / 8, (189 / 255, 189 / 255, 189 / 255)),
+ (4 / 8, (150 / 255, 150 / 255, 150 / 255)),
+ (5 / 8, (115 / 255, 115 / 255, 115 / 255)),
+ (6 / 8, (82 / 255, 82 / 255, 82 / 255)),
+ (7 / 8, (37 / 255, 37 / 255, 37 / 255)),
+ (8 / 8, (0 / 255, 0 / 255, 0 / 255)),
+)
+
+oranges = Colormap(
+ (0 / 8, (255 / 255, 245 / 255, 235 / 255)),
+ (1 / 8, (254 / 255, 230 / 255, 206 / 255)),
+ (2 / 8, (253 / 255, 208 / 255, 162 / 255)),
+ (3 / 8, (253 / 255, 174 / 255, 107 / 255)),
+ (4 / 8, (253 / 255, 141 / 255, 60 / 255)),
+ (5 / 8, (241 / 255, 105 / 255, 19 / 255)),
+ (6 / 8, (217 / 255, 72 / 255, 1 / 255)),
+ (7 / 8, (166 / 255, 54 / 255, 3 / 255)),
+ (8 / 8, (127 / 255, 39 / 255, 4 / 255)),
+)
+
+orrd = Colormap(
+ (0 / 8, (255 / 255, 247 / 255, 236 / 255)),
+ (1 / 8, (254 / 255, 232 / 255, 200 / 255)),
+ (2 / 8, (253 / 255, 212 / 255, 158 / 255)),
+ (3 / 8, (253 / 255, 187 / 255, 132 / 255)),
+ (4 / 8, (252 / 255, 141 / 255, 89 / 255)),
+ (5 / 8, (239 / 255, 101 / 255, 72 / 255)),
+ (6 / 8, (215 / 255, 48 / 255, 31 / 255)),
+ (7 / 8, (179 / 255, 0 / 255, 0 / 255)),
+ (8 / 8, (127 / 255, 0 / 255, 0 / 255)),
+)
+
+pubu = Colormap(
+ (0 / 8, (255 / 255, 247 / 255, 251 / 255)),
+ (1 / 8, (236 / 255, 231 / 255, 242 / 255)),
+ (2 / 8, (208 / 255, 209 / 255, 230 / 255)),
+ (3 / 8, (166 / 255, 189 / 255, 219 / 255)),
+ (4 / 8, (116 / 255, 169 / 255, 207 / 255)),
+ (5 / 8, (54 / 255, 144 / 255, 192 / 255)),
+ (6 / 8, (5 / 255, 112 / 255, 176 / 255)),
+ (7 / 8, (4 / 255, 90 / 255, 141 / 255)),
+ (8 / 8, (2 / 255, 56 / 255, 88 / 255)),
+)
+
+pubugn = Colormap(
+ (0 / 8, (255 / 255, 247 / 255, 251 / 255)),
+ (1 / 8, (236 / 255, 226 / 255, 240 / 255)),
+ (2 / 8, (208 / 255, 209 / 255, 230 / 255)),
+ (3 / 8, (166 / 255, 189 / 255, 219 / 255)),
+ (4 / 8, (103 / 255, 169 / 255, 207 / 255)),
+ (5 / 8, (54 / 255, 144 / 255, 192 / 255)),
+ (6 / 8, (2 / 255, 129 / 255, 138 / 255)),
+ (7 / 8, (1 / 255, 108 / 255, 89 / 255)),
+ (8 / 8, (1 / 255, 70 / 255, 54 / 255)),
+)
+
+purd = Colormap(
+ (0 / 8, (247 / 255, 244 / 255, 249 / 255)),
+ (1 / 8, (231 / 255, 225 / 255, 239 / 255)),
+ (2 / 8, (212 / 255, 185 / 255, 218 / 255)),
+ (3 / 8, (201 / 255, 148 / 255, 199 / 255)),
+ (4 / 8, (223 / 255, 101 / 255, 176 / 255)),
+ (5 / 8, (231 / 255, 41 / 255, 138 / 255)),
+ (6 / 8, (206 / 255, 18 / 255, 86 / 255)),
+ (7 / 8, (152 / 255, 0 / 255, 67 / 255)),
+ (8 / 8, (103 / 255, 0 / 255, 31 / 255)),
+)
+
+purples = Colormap(
+ (0 / 8, (252 / 255, 251 / 255, 253 / 255)),
+ (1 / 8, (239 / 255, 237 / 255, 245 / 255)),
+ (2 / 8, (218 / 255, 218 / 255, 235 / 255)),
+ (3 / 8, (188 / 255, 189 / 255, 220 / 255)),
+ (4 / 8, (158 / 255, 154 / 255, 200 / 255)),
+ (5 / 8, (128 / 255, 125 / 255, 186 / 255)),
+ (6 / 8, (106 / 255, 81 / 255, 163 / 255)),
+ (7 / 8, (84 / 255, 39 / 255, 143 / 255)),
+ (8 / 8, (63 / 255, 0 / 255, 125 / 255)),
+)
+
+rdpu = Colormap(
+ (0 / 8, (255 / 255, 247 / 255, 243 / 255)),
+ (1 / 8, (253 / 255, 224 / 255, 221 / 255)),
+ (2 / 8, (252 / 255, 197 / 255, 192 / 255)),
+ (3 / 8, (250 / 255, 159 / 255, 181 / 255)),
+ (4 / 8, (247 / 255, 104 / 255, 161 / 255)),
+ (5 / 8, (221 / 255, 52 / 255, 151 / 255)),
+ (6 / 8, (174 / 255, 1 / 255, 126 / 255)),
+ (7 / 8, (122 / 255, 1 / 255, 119 / 255)),
+ (8 / 8, (73 / 255, 0 / 255, 106 / 255)),
+)
+
+reds = Colormap(
+ (0 / 8, (255 / 255, 245 / 255, 240 / 255)),
+ (1 / 8, (254 / 255, 224 / 255, 210 / 255)),
+ (2 / 8, (252 / 255, 187 / 255, 161 / 255)),
+ (3 / 8, (252 / 255, 146 / 255, 114 / 255)),
+ (4 / 8, (251 / 255, 106 / 255, 74 / 255)),
+ (5 / 8, (239 / 255, 59 / 255, 44 / 255)),
+ (6 / 8, (203 / 255, 24 / 255, 29 / 255)),
+ (7 / 8, (165 / 255, 15 / 255, 21 / 255)),
+ (8 / 8, (103 / 255, 0 / 255, 13 / 255)),
+)
+
+ylgn = Colormap(
+ (0 / 8, (255 / 255, 255 / 255, 229 / 255)),
+ (1 / 8, (247 / 255, 252 / 255, 185 / 255)),
+ (2 / 8, (217 / 255, 240 / 255, 163 / 255)),
+ (3 / 8, (173 / 255, 221 / 255, 142 / 255)),
+ (4 / 8, (120 / 255, 198 / 255, 121 / 255)),
+ (5 / 8, (65 / 255, 171 / 255, 93 / 255)),
+ (6 / 8, (35 / 255, 132 / 255, 67 / 255)),
+ (7 / 8, (0 / 255, 104 / 255, 55 / 255)),
+ (8 / 8, (0 / 255, 69 / 255, 41 / 255)),
+)
+
+ylgnbu = Colormap(
+ (0 / 8, (255 / 255, 255 / 255, 217 / 255)),
+ (1 / 8, (237 / 255, 248 / 255, 177 / 255)),
+ (2 / 8, (199 / 255, 233 / 255, 180 / 255)),
+ (3 / 8, (127 / 255, 205 / 255, 187 / 255)),
+ (4 / 8, (65 / 255, 182 / 255, 196 / 255)),
+ (5 / 8, (29 / 255, 145 / 255, 192 / 255)),
+ (6 / 8, (34 / 255, 94 / 255, 168 / 255)),
+ (7 / 8, (37 / 255, 52 / 255, 148 / 255)),
+ (8 / 8, (8 / 255, 29 / 255, 88 / 255)),
+)
+
+ylorbr = Colormap(
+ (0 / 8, (255 / 255, 255 / 255, 229 / 255)),
+ (1 / 8, (255 / 255, 247 / 255, 188 / 255)),
+ (2 / 8, (254 / 255, 227 / 255, 145 / 255)),
+ (3 / 8, (254 / 255, 196 / 255, 79 / 255)),
+ (4 / 8, (254 / 255, 153 / 255, 41 / 255)),
+ (5 / 8, (236 / 255, 112 / 255, 20 / 255)),
+ (6 / 8, (204 / 255, 76 / 255, 2 / 255)),
+ (7 / 8, (153 / 255, 52 / 255, 4 / 255)),
+ (8 / 8, (102 / 255, 37 / 255, 6 / 255)),
+)
+
+ylorrd = Colormap(
+ (0 / 7, (255 / 255, 255 / 255, 204 / 255)),
+ (1 / 7, (255 / 255, 237 / 255, 160 / 255)),
+ (2 / 7, (254 / 255, 217 / 255, 118 / 255)),
+ (3 / 7, (254 / 255, 178 / 255, 76 / 255)),
+ (4 / 7, (253 / 255, 141 / 255, 60 / 255)),
+ (5 / 7, (252 / 255, 78 / 255, 42 / 255)),
+ (6 / 7, (227 / 255, 26 / 255, 28 / 255)),
+ (7 / 7, (177 / 255, 0 / 255, 38 / 255)),
+)
+
+sequential_colormaps = {
+ "blues": blues,
+ "bugn": bugn,
+ "bupu": bupu,
+ "gnbu": gnbu,
+ "greens": greens,
+ "greys": greys,
+ "oranges": oranges,
+ "orrd": orrd,
+ "pubu": pubu,
+ "pubugn": pubugn,
+ "purd": purd,
+ "purples": purples,
+ "rdpu": rdpu,
+ "reds": reds,
+ "ylgn": ylgn,
+ "ylgnbu": ylgnbu,
+ "ylorbr": ylorbr,
+ "ylorrd": ylorrd,
+}
+
+# * Diverging Colormaps *
+
+brbg = Colormap(
+ (0 / 10, (84 / 255, 48 / 255, 5 / 255)),
+ (1 / 10, (140 / 255, 81 / 255, 10 / 255)),
+ (2 / 10, (191 / 255, 129 / 255, 45 / 255)),
+ (3 / 10, (223 / 255, 194 / 255, 125 / 255)),
+ (4 / 10, (246 / 255, 232 / 255, 195 / 255)),
+ (5 / 10, (245 / 255, 245 / 255, 245 / 255)),
+ (6 / 10, (199 / 255, 234 / 255, 229 / 255)),
+ (7 / 10, (128 / 255, 205 / 255, 193 / 255)),
+ (8 / 10, (53 / 255, 151 / 255, 143 / 255)),
+ (9 / 10, (1 / 255, 102 / 255, 94 / 255)),
+ (10 / 10, (0 / 255, 60 / 255, 48 / 255)),
+)
+
+piyg = Colormap(
+ (0 / 10, (142 / 255, 1 / 255, 82 / 255)),
+ (1 / 10, (197 / 255, 27 / 255, 125 / 255)),
+ (2 / 10, (222 / 255, 119 / 255, 174 / 255)),
+ (3 / 10, (241 / 255, 182 / 255, 218 / 255)),
+ (4 / 10, (253 / 255, 224 / 255, 239 / 255)),
+ (5 / 10, (247 / 255, 247 / 255, 247 / 255)),
+ (6 / 10, (230 / 255, 245 / 255, 208 / 255)),
+ (7 / 10, (184 / 255, 225 / 255, 134 / 255)),
+ (8 / 10, (127 / 255, 188 / 255, 65 / 255)),
+ (9 / 10, (77 / 255, 146 / 255, 33 / 255)),
+ (10 / 10, (39 / 255, 100 / 255, 25 / 255)),
+)
+
+prgn = Colormap(
+ (0 / 10, (64 / 255, 0 / 255, 75 / 255)),
+ (1 / 10, (118 / 255, 42 / 255, 131 / 255)),
+ (2 / 10, (153 / 255, 112 / 255, 171 / 255)),
+ (3 / 10, (194 / 255, 165 / 255, 207 / 255)),
+ (4 / 10, (231 / 255, 212 / 255, 232 / 255)),
+ (5 / 10, (247 / 255, 247 / 255, 247 / 255)),
+ (6 / 10, (217 / 255, 240 / 255, 211 / 255)),
+ (7 / 10, (166 / 255, 219 / 255, 160 / 255)),
+ (8 / 10, (90 / 255, 174 / 255, 97 / 255)),
+ (9 / 10, (27 / 255, 120 / 255, 55 / 255)),
+ (10 / 10, (0 / 255, 68 / 255, 27 / 255)),
+)
+
+puor = Colormap(
+ (0 / 10, (127 / 255, 59 / 255, 8 / 255)),
+ (1 / 10, (179 / 255, 88 / 255, 6 / 255)),
+ (2 / 10, (224 / 255, 130 / 255, 20 / 255)),
+ (3 / 10, (253 / 255, 184 / 255, 99 / 255)),
+ (4 / 10, (254 / 255, 224 / 255, 182 / 255)),
+ (5 / 10, (247 / 255, 247 / 255, 247 / 255)),
+ (6 / 10, (216 / 255, 218 / 255, 235 / 255)),
+ (7 / 10, (178 / 255, 171 / 255, 210 / 255)),
+ (8 / 10, (128 / 255, 115 / 255, 172 / 255)),
+ (9 / 10, (84 / 255, 39 / 255, 136 / 255)),
+ (10 / 10, (45 / 255, 0 / 255, 75 / 255)),
+)
+
+rdbu = Colormap(
+ (0 / 10, (103 / 255, 0 / 255, 31 / 255)),
+ (1 / 10, (178 / 255, 24 / 255, 43 / 255)),
+ (2 / 10, (214 / 255, 96 / 255, 77 / 255)),
+ (3 / 10, (244 / 255, 165 / 255, 130 / 255)),
+ (4 / 10, (253 / 255, 219 / 255, 199 / 255)),
+ (5 / 10, (247 / 255, 247 / 255, 247 / 255)),
+ (6 / 10, (209 / 255, 229 / 255, 240 / 255)),
+ (7 / 10, (146 / 255, 197 / 255, 222 / 255)),
+ (8 / 10, (67 / 255, 147 / 255, 195 / 255)),
+ (9 / 10, (33 / 255, 102 / 255, 172 / 255)),
+ (10 / 10, (5 / 255, 48 / 255, 97 / 255)),
+)
+
+rdgy = Colormap(
+ (0 / 10, (103 / 255, 0 / 255, 31 / 255)),
+ (1 / 10, (178 / 255, 24 / 255, 43 / 255)),
+ (2 / 10, (214 / 255, 96 / 255, 77 / 255)),
+ (3 / 10, (244 / 255, 165 / 255, 130 / 255)),
+ (4 / 10, (253 / 255, 219 / 255, 199 / 255)),
+ (5 / 10, (255 / 255, 255 / 255, 255 / 255)),
+ (6 / 10, (224 / 255, 224 / 255, 224 / 255)),
+ (7 / 10, (186 / 255, 186 / 255, 186 / 255)),
+ (8 / 10, (135 / 255, 135 / 255, 135 / 255)),
+ (9 / 10, (77 / 255, 77 / 255, 77 / 255)),
+ (10 / 10, (26 / 255, 26 / 255, 26 / 255)),
+)
+
+rdylbu = Colormap(
+ (0 / 10, (165 / 255, 0 / 255, 38 / 255)),
+ (1 / 10, (215 / 255, 48 / 255, 39 / 255)),
+ (2 / 10, (244 / 255, 109 / 255, 67 / 255)),
+ (3 / 10, (253 / 255, 174 / 255, 97 / 255)),
+ (4 / 10, (254 / 255, 224 / 255, 144 / 255)),
+ (5 / 10, (255 / 255, 255 / 255, 191 / 255)),
+ (6 / 10, (224 / 255, 243 / 255, 248 / 255)),
+ (7 / 10, (171 / 255, 217 / 255, 233 / 255)),
+ (8 / 10, (116 / 255, 173 / 255, 209 / 255)),
+ (9 / 10, (69 / 255, 117 / 255, 180 / 255)),
+ (10 / 10, (49 / 255, 54 / 255, 149 / 255)),
+)
+
+rdylgn = Colormap(
+ (0 / 10, (165 / 255, 0 / 255, 38 / 255)),
+ (1 / 10, (215 / 255, 48 / 255, 39 / 255)),
+ (2 / 10, (244 / 255, 109 / 255, 67 / 255)),
+ (3 / 10, (253 / 255, 174 / 255, 97 / 255)),
+ (4 / 10, (254 / 255, 224 / 255, 139 / 255)),
+ (5 / 10, (255 / 255, 255 / 255, 191 / 255)),
+ (6 / 10, (217 / 255, 239 / 255, 139 / 255)),
+ (7 / 10, (166 / 255, 217 / 255, 106 / 255)),
+ (8 / 10, (102 / 255, 189 / 255, 99 / 255)),
+ (9 / 10, (26 / 255, 152 / 255, 80 / 255)),
+ (10 / 10, (0 / 255, 104 / 255, 55 / 255)),
+)
+
+spectral = Colormap(
+ (0 / 10, (158 / 255, 1 / 255, 66 / 255)),
+ (1 / 10, (213 / 255, 62 / 255, 79 / 255)),
+ (2 / 10, (244 / 255, 109 / 255, 67 / 255)),
+ (3 / 10, (253 / 255, 174 / 255, 97 / 255)),
+ (4 / 10, (254 / 255, 224 / 255, 139 / 255)),
+ (5 / 10, (255 / 255, 255 / 255, 191 / 255)),
+ (6 / 10, (230 / 255, 245 / 255, 152 / 255)),
+ (7 / 10, (171 / 255, 221 / 255, 164 / 255)),
+ (8 / 10, (102 / 255, 194 / 255, 165 / 255)),
+ (9 / 10, (50 / 255, 136 / 255, 189 / 255)),
+ (10 / 10, (94 / 255, 79 / 255, 162 / 255)),
+)
+
+diverging_colormaps = {
+ "brbg": brbg,
+ "piyg": piyg,
+ "prgn": prgn,
+ "puor": puor,
+ "rdbu": rdbu,
+ "rdgy": rdgy,
+ "rdylbu": rdylbu,
+ "rdylgn": rdylgn,
+ "spectral": spectral,
+}
+
+# * Qualitative Colormaps *
+
+accent = Colormap(
+ (0, (127 / 255, 201 / 255, 127 / 255)),
+ (1, (190 / 255, 174 / 255, 212 / 255)),
+ (2, (253 / 255, 192 / 255, 134 / 255)),
+ (3, (255 / 255, 255 / 255, 153 / 255)),
+ (4, (56 / 255, 108 / 255, 176 / 255)),
+ (5, (240 / 255, 2 / 255, 127 / 255)),
+ (6, (191 / 255, 91 / 255, 23 / 255)),
+ (7, (102 / 255, 102 / 255, 102 / 255)),
+)
+
+dark2 = Colormap(
+ (0, (27 / 255, 158 / 255, 119 / 255)),
+ (1, (217 / 255, 95 / 255, 2 / 255)),
+ (2, (117 / 255, 112 / 255, 179 / 255)),
+ (3, (231 / 255, 41 / 255, 138 / 255)),
+ (4, (102 / 255, 166 / 255, 30 / 255)),
+ (5, (230 / 255, 171 / 255, 2 / 255)),
+ (6, (166 / 255, 118 / 255, 29 / 255)),
+ (7, (102 / 255, 102 / 255, 102 / 255)),
+)
+
+paired = Colormap(
+ (0, (166 / 255, 206 / 255, 227 / 255)),
+ (1, (31 / 255, 120 / 255, 180 / 255)),
+ (2, (178 / 255, 223 / 255, 138 / 255)),
+ (3, (51 / 255, 160 / 255, 44 / 255)),
+ (4, (251 / 255, 154 / 255, 153 / 255)),
+ (5, (227 / 255, 26 / 255, 28 / 255)),
+ (6, (253 / 255, 191 / 255, 111 / 255)),
+ (7, (255 / 255, 127 / 255, 0 / 255)),
+ (8, (202 / 255, 178 / 255, 214 / 255)),
+ (9, (106 / 255, 61 / 255, 154 / 255)),
+ (10, (255 / 255, 255 / 255, 153 / 255)),
+ (11, (177 / 255, 89 / 255, 40 / 255)),
+)
+
+pastel1 = Colormap(
+ (0, (251 / 255, 180 / 255, 174 / 255)),
+ (1, (179 / 255, 205 / 255, 227 / 255)),
+ (2, (204 / 255, 235 / 255, 197 / 255)),
+ (3, (222 / 255, 203 / 255, 228 / 255)),
+ (4, (254 / 255, 217 / 255, 166 / 255)),
+ (5, (255 / 255, 255 / 255, 204 / 255)),
+ (6, (229 / 255, 216 / 255, 189 / 255)),
+ (7, (253 / 255, 218 / 255, 236 / 255)),
+ (8, (242 / 255, 242 / 255, 242 / 255)),
+)
+
+pastel2 = Colormap(
+ (0, (179 / 255, 226 / 255, 205 / 255)),
+ (1, (253 / 255, 205 / 255, 172 / 255)),
+ (2, (203 / 255, 213 / 255, 232 / 255)),
+ (3, (244 / 255, 202 / 255, 228 / 255)),
+ (4, (230 / 255, 245 / 255, 201 / 255)),
+ (5, (255 / 255, 242 / 255, 174 / 255)),
+ (6, (241 / 255, 226 / 255, 204 / 255)),
+ (7, (204 / 255, 204 / 255, 204 / 255)),
+)
+
+set1 = Colormap(
+ (0, (228 / 255, 26 / 255, 28 / 255)),
+ (1, (55 / 255, 126 / 255, 184 / 255)),
+ (2, (77 / 255, 175 / 255, 74 / 255)),
+ (3, (152 / 255, 78 / 255, 163 / 255)),
+ (4, (255 / 255, 127 / 255, 0 / 255)),
+ (5, (255 / 255, 255 / 255, 51 / 255)),
+ (6, (166 / 255, 86 / 255, 40 / 255)),
+ (7, (247 / 255, 129 / 255, 191 / 255)),
+ (8, (153 / 255, 153 / 255, 153 / 255)),
+)
+
+set2 = Colormap(
+ (0, (102 / 255, 194 / 255, 165 / 255)),
+ (1, (252 / 255, 141 / 255, 98 / 255)),
+ (2, (141 / 255, 160 / 255, 203 / 255)),
+ (3, (231 / 255, 138 / 255, 195 / 255)),
+ (4, (166 / 255, 216 / 255, 84 / 255)),
+ (5, (255 / 255, 217 / 255, 47 / 255)),
+ (6, (229 / 255, 196 / 255, 148 / 255)),
+ (7, (179 / 255, 179 / 255, 179 / 255)),
+)
+
+set3 = Colormap(
+ (0, (141 / 255, 211 / 255, 199 / 255)),
+ (1, (255 / 255, 255 / 255, 179 / 255)),
+ (2, (190 / 255, 186 / 255, 218 / 255)),
+ (3, (251 / 255, 128 / 255, 114 / 255)),
+ (4, (128 / 255, 177 / 255, 211 / 255)),
+ (5, (253 / 255, 180 / 255, 98 / 255)),
+ (6, (179 / 255, 222 / 255, 105 / 255)),
+ (7, (252 / 255, 205 / 255, 229 / 255)),
+ (8, (217 / 255, 217 / 255, 217 / 255)),
+ (9, (188 / 255, 128 / 255, 189 / 255)),
+ (10, (204 / 255, 235 / 255, 197 / 255)),
+ (11, (255 / 255, 237 / 255, 111 / 255)),
+)
+
+qualitative_colormaps = {
+ "accent": accent,
+ "dark2": dark2,
+ "paired": paired,
+ "pastel1": pastel1,
+ "pastel2": pastel2,
+ "set1": set1,
+ "set2": set2,
+ "set3": set3,
+}
def colorbar(height, length, colormap, category=False):
=====================================
trollimage/tests/test_image.py
=====================================
@@ -1440,23 +1440,55 @@ class TestXRImage:
with rio.open(tmp.name) as f:
assert f.tags() == tags
- def test_gamma(self):
- """Test gamma correction."""
- arr = np.arange(75).reshape(5, 5, 3) / 75.
- data = xr.DataArray(arr.copy(), dims=['y', 'x', 'bands'],
+ @pytest.mark.parametrize("dtype", (np.float32, np.float64, float))
+ def test_gamma_single_value(self, dtype):
+ """Test gamma correction for one value for all channels."""
+ arr = np.arange(75, dtype=dtype).reshape(5, 5, 3) / 75.
+ data = xr.DataArray(arr, dims=['y', 'x', 'bands'],
coords={'bands': ['R', 'G', 'B']})
img = xrimage.XRImage(data)
img.gamma(.5)
- assert np.allclose(img.data.values, arr ** 2)
+ assert img.data.dtype == dtype
+ np.testing.assert_allclose(img.data.values, arr ** 2)
assert img.data.attrs['enhancement_history'][0] == {'gamma': 0.5}
+ @pytest.mark.parametrize("dtype", (np.float32, np.float64, float))
+ @pytest.mark.parametrize(
+ ("gamma_val"),
+ [
+ (None),
+ (1.0),
+ ([1.0, 1.0, 1.0]),
+ ([None, None, None]),
+ ]
+ )
+ def test_gamma_noop(self, gamma_val, dtype):
+ """Test variety of unity gamma corrections."""
+ arr = np.arange(75, dtype=dtype).reshape(5, 5, 3) / 75.
+ data = xr.DataArray(arr, dims=['y', 'x', 'bands'],
+ coords={'bands': ['R', 'G', 'B']})
+ img = xrimage.XRImage(data)
+ img.gamma(gamma_val)
+ assert img.data.dtype == dtype
+ np.testing.assert_equal(img.data.values, arr)
+ assert 'enhancement_history' not in img.data.attrs
+
+ @pytest.mark.parametrize("dtype", (np.float32, np.float64, float))
+ def test_gamma_per_channel(self, dtype):
+ """Test gamma correction with a value for each channel."""
+ arr = np.arange(75, dtype=dtype).reshape(5, 5, 3) / 75.
+ data = xr.DataArray(arr.copy(), dims=['y', 'x', 'bands'],
+ coords={'bands': ['R', 'G', 'B']})
+ img = xrimage.XRImage(data)
img.gamma([2., 2., 2.])
- assert len(img.data.attrs['enhancement_history']) == 2
- assert np.allclose(img.data.values, arr)
+ assert img.data.dtype == dtype
+ assert img.data.attrs['enhancement_history'][0] == {'gamma': [2.0, 2.0, 2.0]}
+ np.testing.assert_allclose(img.data.values, arr ** 0.5)
- def test_crude_stretch(self):
+ @pytest.mark.parametrize("dtype", (np.float32, np.float64, float))
+ def test_crude_stretch(self, dtype):
"""Check crude stretching."""
- arr = np.arange(75).reshape(5, 5, 3)
+ arr = np.arange(75, dtype=dtype).reshape(5, 5, 3)
data = xr.DataArray(arr.copy(), dims=['y', 'x', 'bands'],
coords={'bands': ['R', 'G', 'B']})
img = xrimage.XRImage(data)
@@ -1467,22 +1499,39 @@ class TestXRImage:
enhs = img.data.attrs['enhancement_history'][0]
scale_expected = np.array([0.01388889, 0.01388889, 0.01388889])
offset_expected = np.array([0., -0.01388889, -0.02777778])
+ assert img.data.dtype == dtype
np.testing.assert_allclose(enhs['scale'].values, scale_expected)
np.testing.assert_allclose(enhs['offset'].values, offset_expected)
- np.testing.assert_allclose(red, arr[:, :, 0] / 72.)
- np.testing.assert_allclose(green, (arr[:, :, 1] - 1.) / (73. - 1.))
- np.testing.assert_allclose(blue, (arr[:, :, 2] - 2.) / (74. - 2.))
+ expected_red = arr[:, :, 0] / 72.
+ np.testing.assert_allclose(red, expected_red.astype(dtype), rtol=1e-6)
+ expected_green = (arr[:, :, 1] - 1.) / (73. - 1.)
+ np.testing.assert_allclose(green, expected_green.astype(dtype), rtol=1e-6)
+ expected_blue = (arr[:, :, 2] - 2.) / (74. - 2.)
+ np.testing.assert_allclose(blue, expected_blue.astype(dtype), rtol=1e-6)
+
+ @pytest.mark.parametrize("dtype", (np.float32, np.float64, float))
+ def test_crude_stretch_with_limits(self, dtype):
+ arr = np.arange(75, dtype=dtype).reshape(5, 5, 3)
+ data = xr.DataArray(arr.copy(), dims=['y', 'x', 'bands'],
+ coords={'bands': ['R', 'G', 'B']})
+ img = xrimage.XRImage(data)
+ img.crude_stretch(0, 74)
+ assert img.data.dtype == dtype
+ np.testing.assert_allclose(img.data.values, arr / 74., rtol=1e-6)
- arr = np.arange(75).reshape(5, 5, 3).astype(float)
+ def test_crude_stretch_integer_data(self):
+ arr = np.arange(75, dtype=int).reshape(5, 5, 3)
data = xr.DataArray(arr.copy(), dims=['y', 'x', 'bands'],
coords={'bands': ['R', 'G', 'B']})
img = xrimage.XRImage(data)
img.crude_stretch(0, 74)
- np.testing.assert_allclose(img.data.values, arr / 74.)
+ assert img.data.dtype == np.float32
+ np.testing.assert_allclose(img.data.values, arr.astype(np.float32) / 74., rtol=1e-6)
- def test_invert(self):
+ @pytest.mark.parametrize("dtype", (np.float32, np.float64, float))
+ def test_invert(self, dtype):
"""Check inversion of the image."""
- arr = np.arange(75).reshape(5, 5, 3) / 75.
+ arr = np.arange(75, dtype=dtype).reshape(5, 5, 3) / 75.
data = xr.DataArray(arr.copy(), dims=['y', 'x', 'bands'],
coords={'bands': ['R', 'G', 'B']})
img = xrimage.XRImage(data)
@@ -1490,6 +1539,7 @@ class TestXRImage:
img.invert(True)
enhs = img.data.attrs['enhancement_history'][0]
assert enhs == {'scale': -1, 'offset': 1}
+ assert img.data.dtype == dtype
assert np.allclose(img.data.values, 1 - arr)
data = xr.DataArray(arr.copy(), dims=['y', 'x', 'bands'],
@@ -1503,13 +1553,15 @@ class TestXRImage:
coords={'bands': ['R', 'G', 'B']})
np.testing.assert_allclose(img.data.values, (data * scale + offset).values)
- def test_linear_stretch(self):
+ @pytest.mark.parametrize("dtype", (np.float32, np.float64, float))
+ def test_linear_stretch(self, dtype):
"""Test linear stretching with cutoffs."""
- arr = np.arange(75).reshape(5, 5, 3) / 74.
+ arr = np.arange(75, dtype=dtype).reshape(5, 5, 3) / 74.
data = xr.DataArray(arr.copy(), dims=['y', 'x', 'bands'],
coords={'bands': ['R', 'G', 'B']})
img = xrimage.XRImage(data)
img.stretch_linear()
+ assert img.data.dtype == dtype
enhs = img.data.attrs['enhancement_history'][0]
np.testing.assert_allclose(enhs['scale'].values, np.array([1.03815937, 1.03815937, 1.03815937]))
np.testing.assert_allclose(enhs['offset'].values, np.array([-0.00505051, -0.01907969, -0.03310887]), atol=1e-8)
@@ -1537,19 +1589,59 @@ class TestXRImage:
[0.878788, 0.878788, 0.878788],
[0.920875, 0.920875, 0.920875],
[0.962963, 0.962963, 0.962963],
- [1.005051, 1.005051, 1.005051]]])
+ [1.005051, 1.005051, 1.005051]]], dtype=dtype)
+
+ np.testing.assert_allclose(img.data.values, res, atol=1.e-6)
+
+ @pytest.mark.parametrize("dtype", (np.float32, np.float64, float))
+ def test_linear_stretch_does_not_affect_alpha(self, dtype):
+ """Test linear stretching with cutoffs."""
+ arr = np.arange(100, dtype=dtype).reshape(5, 5, 4) / 74.
+ arr[:, :, -1] = 1 # alpha channel, fully opaque
+ data = xr.DataArray(arr.copy(), dims=['y', 'x', 'bands'],
+ coords={'bands': ['R', 'G', 'B', 'A']})
+ img = xrimage.XRImage(data)
+ img.stretch_linear((0.005, 0.005))
+ assert img.data.dtype == dtype
+ res = np.array([[[-0.005051, -0.005051, -0.005051, 1.],
+ [0.037037, 0.037037, 0.037037, 1.],
+ [0.079125, 0.079125, 0.079125, 1.],
+ [0.121212, 0.121212, 0.121212, 1.],
+ [0.1633, 0.1633, 0.1633, 1.]],
+ [[0.205387, 0.205387, 0.205387, 1.],
+ [0.247475, 0.247475, 0.247475, 1.],
+ [0.289562, 0.289562, 0.289562, 1.],
+ [0.33165, 0.33165, 0.33165, 1.],
+ [0.373737, 0.373737, 0.373737, 1.]],
+ [[0.415825, 0.415825, 0.415825, 1.],
+ [0.457912, 0.457912, 0.457912, 1.],
+ [0.5, 0.5, 0.5, 1.],
+ [0.542088, 0.542088, 0.542088, 1.],
+ [0.584175, 0.584175, 0.584175, 1.]],
+ [[0.626263, 0.626263, 0.626263, 1.],
+ [0.66835, 0.66835, 0.66835, 1.],
+ [0.710438, 0.710438, 0.710438, 1.],
+ [0.752525, 0.752525, 0.752525, 1.],
+ [0.794613, 0.794613, 0.794613, 1.]],
+ [[0.8367, 0.8367, 0.8367, 1.],
+ [0.878788, 0.878788, 0.878788, 1.],
+ [0.920875, 0.920875, 0.920875, 1.],
+ [0.962963, 0.962963, 0.962963, 1.],
+ [1.005051, 1.005051, 1.005051, 1.]]], dtype=dtype)
np.testing.assert_allclose(img.data.values, res, atol=1.e-6)
- def test_histogram_stretch(self):
+ @pytest.mark.parametrize("dtype", (np.float32, np.float64, float))
+ def test_histogram_stretch(self, dtype):
"""Test histogram stretching."""
- arr = np.arange(75).reshape(5, 5, 3) / 74.
+ arr = np.arange(75, dtype=dtype).reshape(5, 5, 3) / 74.
data = xr.DataArray(arr.copy(), dims=['y', 'x', 'bands'],
coords={'bands': ['R', 'G', 'B']})
img = xrimage.XRImage(data)
img.stretch('histogram')
enhs = img.data.attrs['enhancement_history'][0]
assert enhs == {'hist_equalize': True}
+ assert img.data.dtype == dtype
res = np.array([[[0., 0., 0.],
[0.04166667, 0.04166667, 0.04166667],
[0.08333333, 0.08333333, 0.08333333],
@@ -1578,10 +1670,11 @@ class TestXRImage:
[0.875, 0.875, 0.875],
[0.91666667, 0.91666667, 0.91666667],
[0.95833333, 0.95833333, 0.95833333],
- [0.99951172, 0.99951172, 0.99951172]]])
+ [0.99951172, 0.99951172, 0.99951172]]], dtype=dtype)
np.testing.assert_allclose(img.data.values, res, atol=1.e-6)
+ @pytest.mark.parametrize("dtype", (np.float32, np.float64, float))
@pytest.mark.parametrize(
("min_stretch", "max_stretch"),
[
@@ -1590,9 +1683,9 @@ class TestXRImage:
]
)
@pytest.mark.parametrize("base", ["e", "10", "2"])
- def test_logarithmic_stretch(self, min_stretch, max_stretch, base):
+ def test_logarithmic_stretch(self, min_stretch, max_stretch, base, dtype):
"""Test logarithmic strecthing."""
- arr = np.arange(75).reshape(5, 5, 3) / 74.
+ arr = np.arange(75, dtype=dtype).reshape(5, 5, 3) / 74.
data = xr.DataArray(arr.copy(), dims=['y', 'x', 'bands'],
coords={'bands': ['R', 'G', 'B']})
with assert_maximum_dask_computes(0):
@@ -1603,6 +1696,7 @@ class TestXRImage:
base=base)
enhs = img.data.attrs['enhancement_history'][0]
assert enhs == {'log_factor': 100.0}
+ assert img.data.dtype == dtype
res = np.array([[[0., 0., 0.],
[0.35484693, 0.35484693, 0.35484693],
[0.48307087, 0.48307087, 0.48307087],
@@ -1631,21 +1725,23 @@ class TestXRImage:
[0.97131402, 0.97131402, 0.97131402],
[0.98130304, 0.98130304, 0.98130304],
[0.99085269, 0.99085269, 0.99085269],
- [1., 1., 1.]]])
+ [1., 1., 1.]]], dtype=dtype)
np.testing.assert_allclose(img.data.values, res, atol=1.e-6)
- def test_weber_fechner_stretch(self):
+ @pytest.mark.parametrize("dtype", (np.float32, np.float64, float))
+ def test_weber_fechner_stretch(self, dtype):
"""Test applying S=2.3klog10I+C to the data."""
from trollimage import xrimage
- arr = np.arange(75).reshape(5, 5, 3) / 74.
+ arr = np.arange(75., dtype=dtype).reshape(5, 5, 3) / 74.
data = xr.DataArray(arr.copy(), dims=['y', 'x', 'bands'],
coords={'bands': ['R', 'G', 'B']})
img = xrimage.XRImage(data)
img.stretch_weber_fechner(2.5, 0.2)
enhs = img.data.attrs['enhancement_history'][0]
assert enhs == {'weber_fechner': (2.5, 0.2)}
+ assert img.data.dtype == dtype
res = np.array([[[-np.inf, -6.73656795, -5.0037],
[-3.99003723, -3.27083205, -2.71297317],
[-2.25716928, -1.87179258, -1.5379641],
@@ -1674,7 +1770,7 @@ class TestXRImage:
[3.62126886, 3.66063976, 3.69940022],
[3.7375689, 3.7751636, 3.81220131],
[3.84869831, 3.88467015, 3.92013174],
- [3.95509735, 3.98958065, 4.02359478]]])
+ [3.95509735, 3.98958065, 4.02359478]]], dtype=dtype)
np.testing.assert_allclose(img.data.values, res, atol=1.e-6)
@@ -1861,19 +1957,19 @@ class TestXRImage:
from trollimage import xrimage
# background image
- arr1 = np.zeros((2, 2))
+ arr1 = np.zeros((2, 2), dtype=np.float32)
data1 = xr.DataArray(arr1, dims=['y', 'x'])
bkg = xrimage.XRImage(data1)
# image to be stacked
- arr2 = np.full((2, 2), np.nan)
+ arr2 = np.full((2, 2), np.nan, dtype=np.float32)
arr2[0] = 1
data2 = xr.DataArray(arr2, dims=['y', 'x'])
img = xrimage.XRImage(data2)
# expected result
arr3 = arr1.copy()
- arr3[0] = 1
+ arr3[0] = 1.0
data3 = xr.DataArray(arr3, dims=['y', 'x'])
res = xrimage.XRImage(data3)
@@ -1887,26 +1983,27 @@ class TestXRImage:
"""Test merge."""
pass
- def test_blend(self):
+ @pytest.mark.parametrize("dtype", (np.float32, np.float64, float))
+ def test_blend(self, dtype):
"""Test blend."""
from trollimage import xrimage
- core1 = np.arange(75).reshape(5, 5, 3) / 75.0
- alpha1 = np.linspace(0, 1, 25).reshape(5, 5, 1)
+ core1 = np.arange(75, dtype=dtype).reshape(5, 5, 3) / 75.0
+ alpha1 = np.linspace(0, 1, 25, dtype=dtype).reshape(5, 5, 1)
arr1 = np.concatenate([core1, alpha1], 2)
data1 = xr.DataArray(arr1, dims=['y', 'x', 'bands'],
coords={'bands': ['R', 'G', 'B', 'A']})
img1 = xrimage.XRImage(data1)
- core2 = np.arange(75, 0, -1).reshape(5, 5, 3) / 75.0
- alpha2 = np.linspace(1, 0, 25).reshape(5, 5, 1)
+ core2 = np.arange(75, 0, -1, dtype=dtype).reshape(5, 5, 3) / 75.0
+ alpha2 = np.linspace(1, 0, 25, dtype=dtype).reshape(5, 5, 1)
arr2 = np.concatenate([core2, alpha2], 2)
data2 = xr.DataArray(arr2, dims=['y', 'x', 'bands'],
coords={'bands': ['R', 'G', 'B', 'A']})
img2 = xrimage.XRImage(data2)
-
img3 = img1.blend(img2)
+ assert img3.data.dtype == dtype
np.testing.assert_allclose(
(alpha1 + alpha2 * (1 - alpha1)).squeeze(),
img3.data.sel(bands="A"))
@@ -1918,7 +2015,8 @@ class TestXRImage:
[0.768815, 0.72, 0.6728228, 0.62857145, 0.5885714],
[0.55412847, 0.5264665, 0.50666666, 0.495612, 0.49394494],
[0.5020408, 0.52, 0.5476586, 0.5846154, 0.63027024],
- [0.683871, 0.7445614, 0.81142855, 0.8835443, 0.96]]))
+ [0.683871, 0.7445614, 0.81142855, 0.8835443, 0.96]], dtype=dtype),
+ rtol=2e-6)
with pytest.raises(TypeError):
img1.blend("Salekhard")
@@ -2014,7 +2112,7 @@ class TestImageColorize:
img = image.Image(channels=[arr.copy(), alpha], mode="LA")
img.colorize(brbg)
- expected = list(TestXRImageColorize._expected)
+ expected = list(TestXRImageColorize._expected[np.float64])
np.testing.assert_allclose(img.channels[0], expected[0])
np.testing.assert_allclose(img.channels[1], expected[1])
np.testing.assert_allclose(img.channels[2], expected[2])
@@ -2024,67 +2122,114 @@ class TestImageColorize:
class TestXRImageColorize:
"""Test the colorize method of the XRImage class."""
- _expected = np.array([[
- [3.29411723e-01, 3.57655082e-01, 3.86434110e-01, 4.15693606e-01,
- 4.45354600e-01, 4.75400861e-01, 5.05821366e-01, 5.36605929e-01,
- 5.65154978e-01, 5.92088497e-01, 6.19067971e-01, 6.46087246e-01,
- 6.73140324e-01, 7.00221360e-01, 7.27324645e-01],
- [7.52329770e-01, 7.68885184e-01, 7.85480717e-01, 8.02165033e-01,
- 8.18991652e-01, 8.36019205e-01, 8.53311577e-01, 8.70937939e-01,
- 8.84215464e-01, 8.96340860e-01, 9.08470028e-01, 9.20615990e-01,
- 9.32792728e-01, 9.45015153e-01, 9.57299069e-01],
- [9.57098327e-01, 9.40960114e-01, 9.29584947e-01, 9.23677290e-01,
- 9.23753761e-01, 9.30068208e-01, 9.42551542e-01, 9.60784273e-01,
- 9.41109213e-01, 9.19647970e-01, 8.96571901e-01, 8.72056790e-01,
- 8.46282229e-01, 8.19431622e-01, 7.91692975e-01],
- [7.58448192e-01, 7.21741664e-01, 6.84822731e-01, 6.47626513e-01,
- 6.10070647e-01, 5.72048960e-01, 5.33421992e-01, 4.94570855e-01,
- 4.57464094e-01, 4.20002632e-01, 3.82018455e-01, 3.43266518e-01,
- 3.03372572e-01, 2.61727458e-01, 2.17242854e-01],
- [1.89905753e-01, 1.67063022e-01, 1.43524406e-01, 1.18889110e-01,
- 9.24115112e-02, 6.24348956e-02, 2.53761173e-02, 4.08181032e-03,
- 4.27986478e-03, 4.17929690e-03, 3.78662146e-03, 3.12692318e-03,
- 2.24023940e-03, 1.17807264e-03, 3.21825881e-08]],
- [[1.88235327e-01, 2.05148705e-01, 2.22246533e-01, 2.39526068e-01,
- 2.56989487e-01, 2.74629823e-01, 2.92439994e-01, 3.10413422e-01,
- 3.32343814e-01, 3.57065419e-01, 3.82068278e-01, 4.07348961e-01,
- 4.32903760e-01, 4.58728817e-01, 4.84820203e-01],
- [5.12920806e-01, 5.47946930e-01, 5.82732545e-01, 6.17314757e-01,
- 6.51719365e-01, 6.85963748e-01, 7.20058900e-01, 7.54010901e-01,
- 7.76938576e-01, 7.97119666e-01, 8.17286145e-01, 8.37436050e-01,
- 8.57567245e-01, 8.77677444e-01, 8.97764221e-01],
- [9.14974807e-01, 9.26634684e-01, 9.36490370e-01, 9.44572058e-01,
- 9.50936890e-01, 9.55671974e-01, 9.58899694e-01, 9.60784377e-01,
- 9.54533524e-01, 9.48563492e-01, 9.42789228e-01, 9.37126769e-01,
- 9.31494725e-01, 9.25815456e-01, 9.20015949e-01],
- [9.08501736e-01, 8.93232137e-01, 8.77927046e-01, 8.62584949e-01,
- 8.47204357e-01, 8.31783811e-01, 8.16321878e-01, 7.98071154e-01,
- 7.68921238e-01, 7.39943765e-01, 7.11141597e-01, 6.82517728e-01,
- 6.54075286e-01, 6.25817541e-01, 5.97747906e-01],
- [5.70776450e-01, 5.44247780e-01, 5.17943011e-01, 4.91867999e-01,
- 4.66028940e-01, 4.40432405e-01, 4.15085375e-01, 3.90762603e-01,
- 3.67819656e-01, 3.45100714e-01, 3.22617398e-01, 3.00381887e-01,
- 2.78406993e-01, 2.56706267e-01, 2.35294109e-01]],
- [[1.96078164e-02, 2.42548791e-02, 2.74972980e-02, 2.96227786e-02,
- 3.17156285e-02, 3.38568546e-02, 3.60498743e-02, 3.82990372e-02,
- 5.17340107e-02, 7.13424499e-02, 9.00791380e-02, 1.08349520e-01,
- 1.26372958e-01, 1.44280386e-01, 1.62155431e-01],
- [1.84723724e-01, 2.25766583e-01, 2.66872651e-01, 3.08395883e-01,
- 3.50522786e-01, 3.93349758e-01, 4.36919863e-01, 4.81242202e-01,
- 5.19495725e-01, 5.56210021e-01, 5.93054317e-01, 6.30051817e-01,
- 6.67218407e-01, 7.04564489e-01, 7.42096284e-01],
- [7.75703258e-01, 8.04590533e-01, 8.34519408e-01, 8.64314044e-01,
- 8.92841230e-01, 9.19042335e-01, 9.41963593e-01, 9.60784303e-01,
- 9.45735072e-01, 9.32649658e-01, 9.21608884e-01, 9.12665692e-01,
- 9.05844317e-01, 9.01139812e-01, 8.98517976e-01],
- [8.86846758e-01, 8.68087757e-01, 8.49200397e-01, 8.30188000e-01,
- 8.11054008e-01, 7.91801982e-01, 7.72435606e-01, 7.51368872e-01,
- 7.24059052e-01, 6.97016433e-01, 6.70243011e-01, 6.43740898e-01,
- 6.17512331e-01, 5.91559687e-01, 5.65885492e-01],
- [5.39262087e-01, 5.12603461e-01, 4.86221750e-01, 4.60123396e-01,
- 4.34315297e-01, 4.08804859e-01, 3.83600057e-01, 3.58016749e-01,
- 3.31909003e-01, 3.06406088e-01, 2.81515756e-01, 2.57245695e-01,
- 2.33603632e-01, 2.10597439e-01, 1.88235281e-01]]])
+ _expected = {
+ np.float64: np.array([[
+ [3.29411723e-01, 3.57655082e-01, 3.86434110e-01, 4.15693606e-01,
+ 4.45354600e-01, 4.75400861e-01, 5.05821366e-01, 5.36605929e-01,
+ 5.65154978e-01, 5.92088497e-01, 6.19067971e-01, 6.46087246e-01,
+ 6.73140324e-01, 7.00221360e-01, 7.27324645e-01],
+ [7.52329770e-01, 7.68885184e-01, 7.85480717e-01, 8.02165033e-01,
+ 8.18991652e-01, 8.36019205e-01, 8.53311577e-01, 8.70937939e-01,
+ 8.84215464e-01, 8.96340860e-01, 9.08470028e-01, 9.20615990e-01,
+ 9.32792728e-01, 9.45015153e-01, 9.57299069e-01],
+ [9.57098327e-01, 9.40960114e-01, 9.29584947e-01, 9.23677290e-01,
+ 9.23753761e-01, 9.30068208e-01, 9.42551542e-01, 9.60784273e-01,
+ 9.41109213e-01, 9.19647970e-01, 8.96571901e-01, 8.72056790e-01,
+ 8.46282229e-01, 8.19431622e-01, 7.91692975e-01],
+ [7.58448192e-01, 7.21741664e-01, 6.84822731e-01, 6.47626513e-01,
+ 6.10070647e-01, 5.72048960e-01, 5.33421992e-01, 4.94570855e-01,
+ 4.57464094e-01, 4.20002632e-01, 3.82018455e-01, 3.43266518e-01,
+ 3.03372572e-01, 2.61727458e-01, 2.17242854e-01],
+ [1.89905753e-01, 1.67063022e-01, 1.43524406e-01, 1.18889110e-01,
+ 9.24115112e-02, 6.24348956e-02, 2.53761173e-02, 4.08181032e-03,
+ 4.27986478e-03, 4.17929690e-03, 3.78662146e-03, 3.12692318e-03,
+ 2.24023940e-03, 1.17807264e-03, 3.21825881e-08]],
+ [[1.88235327e-01, 2.05148705e-01, 2.22246533e-01, 2.39526068e-01,
+ 2.56989487e-01, 2.74629823e-01, 2.92439994e-01, 3.10413422e-01,
+ 3.32343814e-01, 3.57065419e-01, 3.82068278e-01, 4.07348961e-01,
+ 4.32903760e-01, 4.58728817e-01, 4.84820203e-01],
+ [5.12920806e-01, 5.47946930e-01, 5.82732545e-01, 6.17314757e-01,
+ 6.51719365e-01, 6.85963748e-01, 7.20058900e-01, 7.54010901e-01,
+ 7.76938576e-01, 7.97119666e-01, 8.17286145e-01, 8.37436050e-01,
+ 8.57567245e-01, 8.77677444e-01, 8.97764221e-01],
+ [9.14974807e-01, 9.26634684e-01, 9.36490370e-01, 9.44572058e-01,
+ 9.50936890e-01, 9.55671974e-01, 9.58899694e-01, 9.60784377e-01,
+ 9.54533524e-01, 9.48563492e-01, 9.42789228e-01, 9.37126769e-01,
+ 9.31494725e-01, 9.25815456e-01, 9.20015949e-01],
+ [9.08501736e-01, 8.93232137e-01, 8.77927046e-01, 8.62584949e-01,
+ 8.47204357e-01, 8.31783811e-01, 8.16321878e-01, 7.98071154e-01,
+ 7.68921238e-01, 7.39943765e-01, 7.11141597e-01, 6.82517728e-01,
+ 6.54075286e-01, 6.25817541e-01, 5.97747906e-01],
+ [5.70776450e-01, 5.44247780e-01, 5.17943011e-01, 4.91867999e-01,
+ 4.66028940e-01, 4.40432405e-01, 4.15085375e-01, 3.90762603e-01,
+ 3.67819656e-01, 3.45100714e-01, 3.22617398e-01, 3.00381887e-01,
+ 2.78406993e-01, 2.56706267e-01, 2.35294109e-01]],
+ [[1.96078164e-02, 2.42548791e-02, 2.74972980e-02, 2.96227786e-02,
+ 3.17156285e-02, 3.38568546e-02, 3.60498743e-02, 3.82990372e-02,
+ 5.17340107e-02, 7.13424499e-02, 9.00791380e-02, 1.08349520e-01,
+ 1.26372958e-01, 1.44280386e-01, 1.62155431e-01],
+ [1.84723724e-01, 2.25766583e-01, 2.66872651e-01, 3.08395883e-01,
+ 3.50522786e-01, 3.93349758e-01, 4.36919863e-01, 4.81242202e-01,
+ 5.19495725e-01, 5.56210021e-01, 5.93054317e-01, 6.30051817e-01,
+ 6.67218407e-01, 7.04564489e-01, 7.42096284e-01],
+ [7.75703258e-01, 8.04590533e-01, 8.34519408e-01, 8.64314044e-01,
+ 8.92841230e-01, 9.19042335e-01, 9.41963593e-01, 9.60784303e-01,
+ 9.45735072e-01, 9.32649658e-01, 9.21608884e-01, 9.12665692e-01,
+ 9.05844317e-01, 9.01139812e-01, 8.98517976e-01],
+ [8.86846758e-01, 8.68087757e-01, 8.49200397e-01, 8.30188000e-01,
+ 8.11054008e-01, 7.91801982e-01, 7.72435606e-01, 7.51368872e-01,
+ 7.24059052e-01, 6.97016433e-01, 6.70243011e-01, 6.43740898e-01,
+ 6.17512331e-01, 5.91559687e-01, 5.65885492e-01],
+ [5.39262087e-01, 5.12603461e-01, 4.86221750e-01, 4.60123396e-01,
+ 4.34315297e-01, 4.08804859e-01, 3.83600057e-01, 3.58016749e-01,
+ 3.31909003e-01, 3.06406088e-01, 2.81515756e-01, 2.57245695e-01,
+ 2.33603632e-01, 2.10597439e-01, 1.88235281e-01]]], dtype=np.float64),
+ np.float32: np.array([[
+ [0.32941175, 0.35765505, 0.38643414, 0.4156936 , 0.44535455,
+ 0.47540084, 0.5058213 , 0.53660583, 0.56515497, 0.5920884 ,
+ 0.61906797, 0.64608735, 0.67314035, 0.7002214 , 0.72732455],
+ [0.7523298 , 0.76888514, 0.7854807 , 0.8021649 , 0.8189918 ,
+ 0.8360192 , 0.8533116 , 0.8709379 , 0.88421535, 0.8963408 ,
+ 0.90846986, 0.9206159 , 0.9327928 , 0.9450152 , 0.95729893],
+ [0.9561593 , 0.9379867 , 0.925193 , 0.9186452 , 0.9189398 ,
+ 0.9262958 , 0.9404795 , 0.96078414, 0.9400202 , 0.9179358 ,
+ 0.89463943, 0.8702369 , 0.84483355, 0.8185319 , 0.79143286],
+ [0.75844824, 0.7217417 , 0.6848227 , 0.64762676, 0.61007077,
+ 0.57204896, 0.533422 , 0.49457097, 0.4574643 , 0.42000294,
+ 0.38201877, 0.34326664, 0.30337277, 0.26172766, 0.21724297],
+ [0.18990554, 0.1670632 , 0.14352395, 0.11888929, 0.09241185,
+ 0.06243531, 0.02537645, 0.00408208, 0.0042801 , 0.00417955,
+ 0.00378686, 0.00312716, 0.00224016, 0.00117794, 0. ]],
+ [[0.18823533, 0.20514871, 0.22224654, 0.23952611, 0.25698954,
+ 0.27462983, 0.2924401 , 0.31041354, 0.33234388, 0.35706556,
+ 0.38206834, 0.40734893, 0.4329038 , 0.45872888, 0.4848204 ],
+ [0.51292086, 0.54794705, 0.5827325 , 0.6173149 , 0.65171933,
+ 0.6859637 , 0.720059 , 0.754011 , 0.7769386 , 0.7971198 ,
+ 0.81728625, 0.8374362 , 0.8575672 , 0.87767744, 0.8977643 ],
+ [0.9152645 , 0.92748123, 0.93765223, 0.9458176 , 0.9520587 ,
+ 0.95650315, 0.9593312 , 0.96078444, 0.9547297 , 0.9488435 ,
+ 0.9430687 , 0.9373506 , 0.93163645, 0.9258765 , 0.92002386],
+ [0.9085018 , 0.89323217, 0.8779272 , 0.86258495, 0.84720427,
+ 0.83178383, 0.81632185, 0.79807115, 0.7689212 , 0.73994386,
+ 0.71114165, 0.68251765, 0.65407526, 0.62581754, 0.597748 ],
+ [0.5707766 , 0.5442478 , 0.51794314, 0.49186793, 0.46602884,
+ 0.4404323 , 0.4150853 , 0.39076254, 0.3678196 , 0.34510067,
+ 0.32261738, 0.3003818 , 0.27840695, 0.25670624, 0.23529409]],
+ [[0.01960781, 0.02425484, 0.02749731, 0.02962274, 0.03171561,
+ 0.03385685, 0.03604981, 0.03829896, 0.05173399, 0.07134236,
+ 0.0900792 , 0.10834952, 0.12637301, 0.14428039, 0.16215537],
+ [0.18472369, 0.22576652, 0.26687256, 0.30839586, 0.35052276,
+ 0.39334968, 0.43691984, 0.48124215, 0.51949567, 0.55621 ,
+ 0.59305423, 0.6300518 , 0.6672183 , 0.7045645 , 0.7420964 ],
+ [0.7757837 , 0.80522877, 0.83597785, 0.8665506 , 0.8955321 ,
+ 0.9216227 , 0.9436848 , 0.96078426, 0.94695187, 0.93475634,
+ 0.9242341 , 0.9154071 , 0.9082816 , 0.90284896, 0.8990856 ],
+ [0.8868469 , 0.8680878 , 0.8492005 , 0.83018804, 0.8110541 ,
+ 0.791802 , 0.7724357 , 0.7513689 , 0.7240591 , 0.69701654,
+ 0.67024314, 0.64374095, 0.6175124 , 0.59155977, 0.5658856 ],
+ [0.53926224, 0.5126035 , 0.48622182, 0.4601233 , 0.43431523,
+ 0.40880483, 0.3836 , 0.35801673, 0.331909 , 0.30640608,
+ 0.28151578, 0.25724563, 0.23360366, 0.21059746, 0.18823537]]], dtype=np.float32)}
@pytest.mark.parametrize("colormap_tag", [None, "colormap"])
def test_colorize_geotiff_tag(self, tmp_path, colormap_tag):
@@ -2109,35 +2254,47 @@ class TestXRImageColorize:
np.testing.assert_allclose(new_brbg.colors, loaded_brbg.colors)
@pytest.mark.parametrize(
- ("new_range", "input_scale", "input_offset", "expected_scale", "expected_offset"),
+ ("new_range", "input_scale", "input_offset", "expected_scale", "expected_offset", "dtype"),
[
- ((0.0, 1.0), 1.0, 0.0, 1.0, 0.0),
- ((0.0, 0.5), 1.0, 0.0, 2.0, 0.0),
- ((2.0, 4.0), 2.0, 2.0, 0.5, -1.0),
+ ((0.0, 1.0), 1.0, 0.0, 1.0, 0.0, np.float32),
+ ((0.0, 0.5), 1.0, 0.0, 2.0, 0.0, np.float32),
+ ((2.0, 4.0), 2.0, 2.0, 0.5, -1.0, np.float32),
+ ((0.0, 1.0), 1.0, 0.0, 1.0, 0.0, np.float64),
+ ((0.0, 0.5), 1.0, 0.0, 2.0, 0.0, np.float64),
+ ((2.0, 4.0), 2.0, 2.0, 0.5, -1.0, np.float64),
],
)
- def test_colorize_l_rgb(self, new_range, input_scale, input_offset, expected_scale, expected_offset):
- """Test colorize with an RGB colormap."""
- arr = np.arange(75).reshape(5, 15) / 74. * input_scale + input_offset
- data = xr.DataArray(arr.copy(), dims=['y', 'x'])
+ def test_colorize_l_rgb(self, new_range, input_scale, input_offset, expected_scale, expected_offset, dtype):
+ """Test colorize with a RGB colormap."""
+ img = self._get_input_image(dtype, input_scale, input_offset)
new_brbg = brbg.set_range(*new_range, inplace=False)
- img = xrimage.XRImage(data)
img.colorize(new_brbg)
values = img.data.compute()
+ assert values.dtype == dtype
+ expected = self._get_expected_colorize_l_rgb(new_range, dtype)
+ np.testing.assert_allclose(values, expected, atol=1e-6)
+ assert "enhancement_history" in img.data.attrs
+ assert img.data.attrs["enhancement_history"][-1]["scale"] == expected_scale
+ assert img.data.attrs["enhancement_history"][-1]["offset"] == expected_offset
+ assert isinstance(img.data.attrs["enhancement_history"][-1]["colormap"], Colormap)
+
+ @staticmethod
+ def _get_input_image(dtype, input_scale, input_offset):
+ arr = np.arange(75, dtype=dtype).reshape(5, 15) / 74. * input_scale + input_offset
+ data = xr.DataArray(arr, dims=['y', 'x'])
+ return xrimage.XRImage(data)
+
+ def _get_expected_colorize_l_rgb(self, new_range, dtype):
if new_range[1] == 0.5:
- expected2 = self._expected.copy().reshape((3, 75))
- flat_expected = self._expected.reshape((3, 75))
+ expected2 = self._expected[dtype].copy().reshape((3, 75))
+ flat_expected = self._expected[dtype].reshape((3, 75))
expected2[:, :38] = flat_expected[:, ::2]
expected2[:, 38:] = flat_expected[:, -1:]
expected = expected2.reshape((3, 5, 15))
else:
- expected = self._expected
- np.testing.assert_allclose(values, expected)
- assert "enhancement_history" in img.data.attrs
- assert img.data.attrs["enhancement_history"][-1]["scale"] == expected_scale
- assert img.data.attrs["enhancement_history"][-1]["offset"] == expected_offset
- assert isinstance(img.data.attrs["enhancement_history"][-1]["colormap"], Colormap)
+ expected = self._expected[dtype]
+ return expected
def test_colorize_int_l_rgb_with_fills(self):
"""Test integer data with _FillValue is masked (NaN) when colorized."""
@@ -2149,6 +2306,8 @@ class TestXRImageColorize:
img = xrimage.XRImage(data)
img.colorize(new_brbg)
values = img.data.compute()
+ # Integer data inherits dtype from the colormap when colorized
+ assert values.dtype == new_brbg.colors.dtype
assert values.shape == (3,) + arr.shape # RGB
np.testing.assert_allclose(values[:, 1, :], np.nan)
assert np.count_nonzero(np.isnan(values)) == arr.shape[1] * 3
@@ -2169,7 +2328,7 @@ class TestXRImageColorize:
img.colorize(brbg)
values = img.data.values
- expected = np.concatenate((self._expected,
+ expected = np.concatenate((self._expected[np.float64],
alpha.reshape((1,) + alpha.shape)))
np.testing.assert_allclose(values, expected)
assert "enhancement_history" in img.data.attrs
@@ -2234,6 +2393,7 @@ class TestXRImagePalettize:
expected2[:, :38] = flat_expected[:, ::2]
expected2[:, 38:] = flat_expected[:, -1:]
expected = expected2.reshape((1, 5, 15))
+ assert np.issubdtype(values.dtype, np.integer)
np.testing.assert_allclose(values, expected)
assert "enhancement_history" in img.data.attrs
assert img.data.attrs["enhancement_history"][-1]["scale"] == expected_scale
@@ -2289,10 +2449,10 @@ class TestXRImageSaveScaleOffset:
def setup_method(self) -> None:
"""Set up the test case."""
from trollimage import xrimage
- data = xr.DataArray(np.arange(25).reshape(5, 5, 1), dims=[
+ data = xr.DataArray(np.arange(25, dtype=np.float32).reshape(5, 5, 1), dims=[
'y', 'x', 'bands'], coords={'bands': ['L']})
self.img = xrimage.XRImage(data)
- rgb_data = xr.DataArray(np.arange(3 * 25).reshape(5, 5, 3), dims=[
+ rgb_data = xr.DataArray(np.arange(3 * 25, dtype=np.float32).reshape(5, 5, 3), dims=[
'y', 'x', 'bands'], coords={'bands': ['R', 'G', 'B']})
self.rgb_img = xrimage.XRImage(rgb_data)
=====================================
trollimage/version.py
=====================================
@@ -26,9 +26,9 @@ def get_keywords():
# setup.py/versioneer.py will grep for the variable names, so they must
# each be defined on a line of their own. _version.py will just call
# get_keywords().
- git_refnames = " (tag: v1.21.0)"
- git_full = "57116d66bcbffdecb4b19555ca0b858d80a222d6"
- git_date = "2023-09-04 20:03:38 -0500"
+ git_refnames = " (HEAD -> main, tag: v1.22.0)"
+ git_full = "dd16cdfbf40d8a245454ab5675166a1e9282b20c"
+ git_date = "2023-11-23 08:59:37 +0100"
keywords = {"refnames": git_refnames, "full": git_full, "date": git_date}
return keywords
=====================================
trollimage/xrimage.py
=====================================
@@ -344,7 +344,7 @@ class XRImage:
warnings.warn(
"include_scale_offset_tags is deprecated, please use "
"scale_offset_tags to indicate tag labels",
- DeprecationWarning)
+ DeprecationWarning, stacklevel=2)
scale_offset_tags = scale_offset_tags or ("scale", "offset")
if tags is None:
@@ -659,7 +659,7 @@ class XRImage:
"Specified fill value will overlap with valid "
"data. To avoid this warning specify a fill_value "
"that is the minimum or maximum for the data type "
- "being saved to.")
+ "being saved to.", stacklevel=3)
return scale, offset
def _scale_to_dtype(self, data, dtype, fill_value=None):
@@ -940,18 +940,28 @@ class XRImage:
the normal [0,1] range of the channels.
"""
- if isinstance(gamma, (list, tuple)):
- gamma = self.xrify_tuples(gamma)
- elif gamma is None or gamma == 1.0:
+ if _is_unity_or_none(gamma):
return
+ inverse_gamma = self._get_inverse_gamma(gamma)
logger.debug("Applying gamma %s", str(gamma))
attrs = self.data.attrs
self.data = self.data.clip(min=0)
- self.data **= 1.0 / gamma
+ self.data **= inverse_gamma
self.data.attrs = attrs
self.data.attrs.setdefault('enhancement_history', []).append({'gamma': gamma})
+ def _get_inverse_gamma(self, gamma):
+ if np.issubdtype(self.data.dtype, np.floating):
+ dtype = self.data.dtype
+ else:
+ dtype = np.float32
+ if isinstance(gamma, (list, tuple)):
+ gamma = self.xrify_tuples(gamma).astype(dtype)
+ else:
+ gamma = np.array(gamma, dtype=dtype)
+ return 1.0 / gamma
+
def stretch(self, stretch="crude", **kwargs):
"""Apply stretching to the current image.
@@ -1019,29 +1029,45 @@ class XRImage:
"""
logger.debug("Perform a linear contrast stretch.")
+ left, right = self._get_left_and_right_quantiles_for_linear_stretch(cutoffs)
+
+ self.crude_stretch(left, right)
+
+ def _get_left_and_right_quantiles_for_linear_stretch(self, cutoffs):
logger.debug("Calculate the histogram quantiles: ")
logger.debug("Left and right quantiles: " +
str(cutoffs[0]) + " " + str(cutoffs[1]))
-
cutoff_type = np.float64
# numpy percentile (which quantile calls) returns 64-bit floats
# unless the value is a higher order float
if np.issubdtype(self.data.dtype, np.floating) and \
np.dtype(self.data.dtype).itemsize > 8:
cutoff_type = self.data.dtype
- left, right = dask.delayed(self._compute_quantile, nout=2)(self.data.data, self.data.dims, cutoffs)
- left_data = da.from_delayed(left,
- shape=(self.data.sizes['bands'],),
- dtype=cutoff_type)
+
+ data = self.data
+ if 'A' in self.data.coords['bands'].values:
+ data = self.data.sel(bands=self.data.coords['bands'].values[:-1])
+
+ left_data, right_data = self._get_left_and_right_quantiles_without_alpha(data, cutoffs, cutoff_type)
+
+ if 'A' in self.data.coords['bands'].values:
+ left_data = np.hstack([left_data, np.array([0])])
+ right_data = np.hstack([right_data, np.array([1])])
left = xr.DataArray(left_data, dims=('bands',),
coords={'bands': self.data['bands']})
- right_data = da.from_delayed(right,
- shape=(self.data.sizes['bands'],),
- dtype=cutoff_type)
right = xr.DataArray(right_data, dims=('bands',),
coords={'bands': self.data['bands']})
+ return left, right
- self.crude_stretch(left, right)
+ def _get_left_and_right_quantiles_without_alpha(self, data, cutoffs, cutoff_type):
+ left, right = dask.delayed(self._compute_quantile, nout=2)(data.data, data.dims, cutoffs)
+ left_data = da.from_delayed(left,
+ shape=(data.sizes['bands'],),
+ dtype=cutoff_type)
+ right_data = da.from_delayed(right,
+ shape=(data.sizes['bands'],),
+ dtype=cutoff_type)
+ return left_data, right_data
def crude_stretch(self, min_stretch=None, max_stretch=None):
"""Perform simple linear stretching.
@@ -1050,31 +1076,47 @@ class XRImage:
normalizes to the [0,1] range.
"""
- if min_stretch is None:
- non_band_dims = tuple(x for x in self.data.dims if x != 'bands')
- min_stretch = self.data.min(dim=non_band_dims)
- if max_stretch is None:
+ min_stretch = self._check_stretch_value(min_stretch, kind='min')
+ max_stretch = self._check_stretch_value(max_stretch, kind='max')
+ scale_factor = self._get_scale_factor(min_stretch, max_stretch)
+
+ attrs = self.data.attrs
+ offset = -min_stretch * scale_factor
+ self.data = np.multiply(self.data, scale_factor, dtype=scale_factor.dtype) + offset
+ self.data.attrs = attrs
+ self.data.attrs.setdefault('enhancement_history', []).append({'scale': scale_factor,
+ 'offset': offset})
+
+ def _check_stretch_value(self, val, kind='min'):
+ if val is None:
non_band_dims = tuple(x for x in self.data.dims if x != 'bands')
- max_stretch = self.data.max(dim=non_band_dims)
+ val = getattr(self.data, kind)(dim=non_band_dims)
- if isinstance(min_stretch, (list, tuple)):
- min_stretch = self.xrify_tuples(min_stretch)
- if isinstance(max_stretch, (list, tuple)):
- max_stretch = self.xrify_tuples(max_stretch)
+ if isinstance(val, (list, tuple)):
+ val = self.xrify_tuples(val)
+
+ try:
+ val = val.astype(self.data.dtype)
+ except AttributeError:
+ val = self.data.dtype.type(val)
+ return val
+
+ def _get_scale_factor(self, min_stretch, max_stretch):
delta = (max_stretch - min_stretch)
+ dtype = self._infer_scale_factor_dtype()
if isinstance(delta, xr.DataArray):
# fillna if delta is NaN
- scale_factor = (1.0 / delta).fillna(0)
+ scale_factor = (1.0 / delta).fillna(0).astype(dtype)
else:
- scale_factor = 1.0 / delta
- attrs = self.data.attrs
- offset = -min_stretch * scale_factor
- self.data *= scale_factor
- self.data += offset
- self.data.attrs = attrs
- self.data.attrs.setdefault('enhancement_history', []).append({'scale': scale_factor,
- 'offset': offset})
+ scale_factor = np.array(1.0 / delta, dtype=dtype)
+
+ return scale_factor
+
+ def _infer_scale_factor_dtype(self):
+ if np.issubdtype(self.data.dtype, np.integer):
+ return np.float32
+ return self.data.dtype
def stretch_hist_equalize(self, approximate=False):
"""Stretch the current image's colors through histogram equalization.
@@ -1284,6 +1326,7 @@ class XRImage:
if self.mode not in ("L", "LA"):
raise ValueError("Image should be grayscale to colorize")
+ colormap = self._adjust_colormap_dtype(colormap)
l_data = self._get_masked_floating_luminance_data()
alpha = self.data.sel(bands=['A']) if self.mode == "LA" else None
new_data = colormap.colorize(l_data.data)
@@ -1309,6 +1352,12 @@ class XRImage:
'colormap': colormap,
})
+ def _adjust_colormap_dtype(self, colormap):
+ if np.issubdtype(self.data.dtype, np.floating) and colormap.colors.dtype != self.data.dtype:
+ colormap.colors = colormap.colors.astype(self.data.dtype)
+ colormap.values = colormap.values.astype(self.data.dtype)
+ return colormap
+
def _get_masked_floating_luminance_data(self):
l_data = self.data.sel(bands='L')
# mask any integer fields with _FillValue
@@ -1468,3 +1517,11 @@ class XRImage:
b = io.BytesIO()
self.pil_image().save(b, format='png')
return b.getvalue()
+
+
+def _is_unity_or_none(gamma):
+ if gamma is None or gamma == 1.0:
+ return True
+ if not hasattr(gamma, "__iter__"):
+ return False
+ return all(g == 1.0 for g in gamma) or all(g is None for g in gamma)
View it on GitLab: https://salsa.debian.org/debian-gis-team/trollimage/-/compare/a0c2b213874783926666ec2a612b3bf512a9aa6e...233e21d4ab458a7d7e388c023f5cca28367f74b0
--
View it on GitLab: https://salsa.debian.org/debian-gis-team/trollimage/-/compare/a0c2b213874783926666ec2a612b3bf512a9aa6e...233e21d4ab458a7d7e388c023f5cca28367f74b0
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